diff --git a/.DS_Store b/.DS_Store
index 464e47ece3ae74319af96f7c6136448c559eefd9..b1a9e265185ca99cf597628b81e4623453f60f5e 100644
Binary files a/.DS_Store and b/.DS_Store differ
diff --git a/.idea/.gitignore b/.idea/.gitignore
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/.idea/inspectionProfiles/Project_Default.xml b/.idea/inspectionProfiles/Project_Default.xml
deleted file mode 100644
index 3c67f7c298a3cde16771d4c839e31c227ff0ebcd..0000000000000000000000000000000000000000
--- a/.idea/inspectionProfiles/Project_Default.xml
+++ /dev/null
@@ -1,15 +0,0 @@
-<component name="InspectionProjectProfileManager">
- <profile version="1.0">
- <option name="myName" value="Project Default" />
- <inspection_tool class="PyPackageRequirementsInspection" enabled="true" level="WARNING" enabled_by_default="true">
- <option name="ignoredPackages">
- <value>
- <list size="2">
- <item index="0" class="java.lang.String" itemvalue="fastapi" />
- <item index="1" class="java.lang.String" itemvalue="uvicorn" />
- </list>
- </value>
- </option>
- </inspection_tool>
- </profile>
-</component>
\ No newline at end of file
diff --git a/.idea/inspectionProfiles/profiles_settings.xml b/.idea/inspectionProfiles/profiles_settings.xml
deleted file mode 100644
index 105ce2da2d6447d11dfe32bfb846c3d5b199fc99..0000000000000000000000000000000000000000
--- a/.idea/inspectionProfiles/profiles_settings.xml
+++ /dev/null
@@ -1,6 +0,0 @@
-<component name="InspectionProjectProfileManager">
- <settings>
- <option name="USE_PROJECT_PROFILE" value="false" />
- <version value="1.0" />
- </settings>
-</component>
\ No newline at end of file
diff --git a/.idea/machine-learning-services.iml b/.idea/machine-learning-services.iml
deleted file mode 100644
index d0876a78d06ac03b5d78c8dcdb95570281c6f1d6..0000000000000000000000000000000000000000
--- a/.idea/machine-learning-services.iml
+++ /dev/null
@@ -1,8 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<module type="PYTHON_MODULE" version="4">
- <component name="NewModuleRootManager">
- <content url="file://$MODULE_DIR$" />
- <orderEntry type="inheritedJdk" />
- <orderEntry type="sourceFolder" forTests="false" />
- </component>
-</module>
\ No newline at end of file
diff --git a/.idea/modules.xml b/.idea/modules.xml
deleted file mode 100644
index 03d4720ac1aa8f516cc9418f2dd0781005bbd876..0000000000000000000000000000000000000000
--- a/.idea/modules.xml
+++ /dev/null
@@ -1,8 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
- <component name="ProjectModuleManager">
- <modules>
- <module fileurl="file://$PROJECT_DIR$/.idea/machine-learning-services.iml" filepath="$PROJECT_DIR$/.idea/machine-learning-services.iml" />
- </modules>
- </component>
-</project>
\ No newline at end of file
diff --git a/.idea/vcs.xml b/.idea/vcs.xml
deleted file mode 100644
index 94a25f7f4cb416c083d265558da75d457237d671..0000000000000000000000000000000000000000
--- a/.idea/vcs.xml
+++ /dev/null
@@ -1,6 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
- <component name="VcsDirectoryMappings">
- <mapping directory="$PROJECT_DIR$" vcs="Git" />
- </component>
-</project>
\ No newline at end of file
diff --git a/.idea/workspace.xml b/.idea/workspace.xml
deleted file mode 100644
index c4882d3d966c570abf81c3d09bf6795986b9371d..0000000000000000000000000000000000000000
--- a/.idea/workspace.xml
+++ /dev/null
@@ -1,40 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
- <component name="ChangeListManager">
- <list default="true" id="abf645dd-868d-4c80-961c-2d004fc1985a" name="Changes" comment="" />
- <option name="SHOW_DIALOG" value="false" />
- <option name="HIGHLIGHT_CONFLICTS" value="true" />
- <option name="HIGHLIGHT_NON_ACTIVE_CHANGELIST" value="false" />
- <option name="LAST_RESOLUTION" value="IGNORE" />
- </component>
- <component name="Git.Settings">
- <option name="RECENT_GIT_ROOT_PATH" value="$PROJECT_DIR$" />
- </component>
- <component name="MarkdownSettingsMigration">
- <option name="stateVersion" value="1" />
- </component>
- <component name="ProjectId" id="2hjFmzxXfQw6Vnyh3x0lrokQV5A" />
- <component name="ProjectLevelVcsManager" settingsEditedManually="true" />
- <component name="ProjectViewState">
- <option name="hideEmptyMiddlePackages" value="true" />
- <option name="showLibraryContents" value="true" />
- </component>
- <component name="PropertiesComponent"><![CDATA[{
- "keyToString": {
- "RunOnceActivity.OpenProjectViewOnStart": "true",
- "RunOnceActivity.ShowReadmeOnStart": "true",
- "last_opened_file_path": "/Users/patrickschnepf/Desktop/Digital Business/6tes Semester/Tech Inno/machine-learning-services"
- }
-}]]></component>
- <component name="SpellCheckerSettings" RuntimeDictionaries="0" Folders="0" CustomDictionaries="0" DefaultDictionary="application-level" UseSingleDictionary="true" transferred="true" />
- <component name="TaskManager">
- <task active="true" id="Default" summary="Default task">
- <changelist id="abf645dd-868d-4c80-961c-2d004fc1985a" name="Changes" comment="" />
- <created>1718099746648</created>
- <option name="number" value="Default" />
- <option name="presentableId" value="Default" />
- <updated>1718099746648</updated>
- </task>
- <servers />
- </component>
-</project>
\ No newline at end of file
diff --git a/.virtual_documents/Marketing/Generation of Individual Playlists/notebook.ipynb b/.virtual_documents/Marketing/Generation of Individual Playlists/notebook.ipynb
deleted file mode 100644
index af53a8d5df11121ab9f6a8b75710fa2a009e5f86..0000000000000000000000000000000000000000
--- a/.virtual_documents/Marketing/Generation of Individual Playlists/notebook.ipynb
+++ /dev/null
@@ -1,185 +0,0 @@
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-# @hidden_cell
-# The project token is an authorization token that is used to access project resources like data sources, connections, and used by platform APIs.
-# from project_lib import Project
-# project = Project(project_id='148a63b8-8cd0-4ae9-acd7-382d6f8da2d1', project_access_token='p-b4ba4eff97677a6d061b1f9eb562ffce54a4e527')
-# pc = project.project_context
-
-
-import numpy as np
-import pandas as pd
-import matplotlib.pyplot as plt
-import seaborn as sns
-sns.set()
-
-
-
-
-
-#Fetch the local file
-# my_file = project.get_file("data.csv")
-
-#Read the CSV data file from the object storage into a pandas DataFrame
-#my_file.seek(0)
-
-#raw_data = pd.read_csv(my_file)
-raw_data = pd.read_csv("https://storage.googleapis.com/ml-service-repository-datastorage/Generation_of_Individual_Playlists_Generation-of-Individual-Playlists-data.csv")
-
-raw_data.head()
-
-
-#raw_data.head(5)
-
-
-#descriptive statistics for all columns
-raw_data.describe(include = 'all')
-
-
-
-
-
-# check for duplicate rows
-raw_data[raw_data.duplicated(keep = False)]
-
-
-
-
-
-raw_data.isnull().sum()
-
-
-
-
-
-#predictive modeling
-#plot a correlation matrix to find out which variables are correlated to each other
-f,ax=plt.subplots(figsize = (18,18))
-sns.heatmap(raw_data.corr(),annot= True,linewidths=0.2, fmt = ".1f",ax=ax)
-plt.xticks(rotation=90)
-plt.yticks(rotation=0)
-plt.title('Correlation Map')
-plt.show()
-
-
-
-
-
-raw_data= raw_data.drop(['name', 'release_date'], axis=1)
-
-
-raw_data = raw_data.drop(['id'], axis=1)
-
-
-raw_data = raw_data.drop(['artists', 'energy'], axis=1)
-
-
-raw_data = raw_data.drop(['year'], axis=1)
-
-
-raw_data.head()
-
-
-f,ax=plt.subplots(figsize = (18,18))
-sns.heatmap(raw_data.corr(),annot= True,linewidths=0.5,fmt = ".1f",ax=ax)
-plt.xticks(rotation=90)
-plt.yticks(rotation=0)
-plt.title('Correlation Map')
-plt.show()
-
-
-#Detect outliers and handle them
-raw_data.hist(figsize=(25,25), bins=50)
-
-
-#raw_data.describe()
-
-
-
-
-
-
-
-
-df_dummies = pd.get_dummies(raw_data, drop_first=True) # 0-1 encoding for categorical values
-df_dummies.head()
-
-
-
-
-
-
-
-
-from sklearn.linear_model import LinearRegression
-from sklearn.preprocessing import StandardScaler
-from sklearn import metrics
-
-
-target = df_dummies['popularity']
-predictors = df_dummies.drop(['popularity'], axis=1)
-
-
-
-
-
-from sklearn.model_selection import train_test_split
-X_train, X_test, y_train, y_test = train_test_split(predictors, target, test_size=0.2, random_state=365)
-
-
-
-
-
-scaler = StandardScaler()
-scaler.fit(X_train)
-
-X_train = scaler.transform(X_train)
-X_test = scaler.transform(X_test)
-
-
-
-
-
-reg = LinearRegression()
-reg.fit(X_train, y_train)
-
-
-print('training performance')
-print(reg.score(X_train,y_train))
-print('test performance')
-print(reg.score(X_test,y_test))
-
-
-y_pred = reg.predict(X_test)
-test = pd.DataFrame({'Predicted':y_pred,'Actual':y_test})
-fig= plt.figure(figsize=(16,8))
-test = test.reset_index()
-test = test.drop(['index'],axis=1)
-plt.plot(test[:50])
-plt.legend(['Actual','Predicted'])
-sns.jointplot(x='Actual',y='Predicted',data=test,kind='reg',);
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/.virtual_documents/Warehouse/Classification of clothing through images/notebook Kopie.ipynb b/.virtual_documents/Warehouse/Classification of clothing through images/notebook Kopie.ipynb
deleted file mode 100644
index af53a8d5df11121ab9f6a8b75710fa2a009e5f86..0000000000000000000000000000000000000000
--- a/.virtual_documents/Warehouse/Classification of clothing through images/notebook Kopie.ipynb
+++ /dev/null
@@ -1,185 +0,0 @@
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-# @hidden_cell
-# The project token is an authorization token that is used to access project resources like data sources, connections, and used by platform APIs.
-# from project_lib import Project
-# project = Project(project_id='148a63b8-8cd0-4ae9-acd7-382d6f8da2d1', project_access_token='p-b4ba4eff97677a6d061b1f9eb562ffce54a4e527')
-# pc = project.project_context
-
-
-import numpy as np
-import pandas as pd
-import matplotlib.pyplot as plt
-import seaborn as sns
-sns.set()
-
-
-
-
-
-#Fetch the local file
-# my_file = project.get_file("data.csv")
-
-#Read the CSV data file from the object storage into a pandas DataFrame
-#my_file.seek(0)
-
-#raw_data = pd.read_csv(my_file)
-raw_data = pd.read_csv("https://storage.googleapis.com/ml-service-repository-datastorage/Generation_of_Individual_Playlists_Generation-of-Individual-Playlists-data.csv")
-
-raw_data.head()
-
-
-#raw_data.head(5)
-
-
-#descriptive statistics for all columns
-raw_data.describe(include = 'all')
-
-
-
-
-
-# check for duplicate rows
-raw_data[raw_data.duplicated(keep = False)]
-
-
-
-
-
-raw_data.isnull().sum()
-
-
-
-
-
-#predictive modeling
-#plot a correlation matrix to find out which variables are correlated to each other
-f,ax=plt.subplots(figsize = (18,18))
-sns.heatmap(raw_data.corr(),annot= True,linewidths=0.2, fmt = ".1f",ax=ax)
-plt.xticks(rotation=90)
-plt.yticks(rotation=0)
-plt.title('Correlation Map')
-plt.show()
-
-
-
-
-
-raw_data= raw_data.drop(['name', 'release_date'], axis=1)
-
-
-raw_data = raw_data.drop(['id'], axis=1)
-
-
-raw_data = raw_data.drop(['artists', 'energy'], axis=1)
-
-
-raw_data = raw_data.drop(['year'], axis=1)
-
-
-raw_data.head()
-
-
-f,ax=plt.subplots(figsize = (18,18))
-sns.heatmap(raw_data.corr(),annot= True,linewidths=0.5,fmt = ".1f",ax=ax)
-plt.xticks(rotation=90)
-plt.yticks(rotation=0)
-plt.title('Correlation Map')
-plt.show()
-
-
-#Detect outliers and handle them
-raw_data.hist(figsize=(25,25), bins=50)
-
-
-#raw_data.describe()
-
-
-
-
-
-
-
-
-df_dummies = pd.get_dummies(raw_data, drop_first=True) # 0-1 encoding for categorical values
-df_dummies.head()
-
-
-
-
-
-
-
-
-from sklearn.linear_model import LinearRegression
-from sklearn.preprocessing import StandardScaler
-from sklearn import metrics
-
-
-target = df_dummies['popularity']
-predictors = df_dummies.drop(['popularity'], axis=1)
-
-
-
-
-
-from sklearn.model_selection import train_test_split
-X_train, X_test, y_train, y_test = train_test_split(predictors, target, test_size=0.2, random_state=365)
-
-
-
-
-
-scaler = StandardScaler()
-scaler.fit(X_train)
-
-X_train = scaler.transform(X_train)
-X_test = scaler.transform(X_test)
-
-
-
-
-
-reg = LinearRegression()
-reg.fit(X_train, y_train)
-
-
-print('training performance')
-print(reg.score(X_train,y_train))
-print('test performance')
-print(reg.score(X_test,y_test))
-
-
-y_pred = reg.predict(X_test)
-test = pd.DataFrame({'Predicted':y_pred,'Actual':y_test})
-fig= plt.figure(figsize=(16,8))
-test = test.reset_index()
-test = test.drop(['index'],axis=1)
-plt.plot(test[:50])
-plt.legend(['Actual','Predicted'])
-sns.jointplot(x='Actual',y='Predicted',data=test,kind='reg',);
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/.virtual_documents/Warehouse/Classification of clothing through images/notebook.ipynb b/.virtual_documents/Warehouse/Classification of clothing through images/notebook.ipynb
deleted file mode 100644
index f9076da0fae11136ad852cc4dbdd36d4ac53c1f4..0000000000000000000000000000000000000000
--- a/.virtual_documents/Warehouse/Classification of clothing through images/notebook.ipynb
+++ /dev/null
@@ -1,339 +0,0 @@
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-pip install tensorflow-datasets
-
-
-import numpy as np
-import pandas as pd
-import matplotlib.pyplot as plt
-import tensorflow as tf
-
-import tensorflow_datasets as tfds
-from tensorflow.keras.models import Sequential
-from tensorflow.keras.layers import Flatten, Dense, Conv2D, MaxPooling2D, Dropout, Layer
-from tensorflow.keras.utils import to_categorical, plot_model
-from sklearn.model_selection import train_test_split
-from sklearn.metrics import classification_report
-
-
-# Should be 2.5.0
-tf.__version__
-
-
-
-
-
-
-
-
-csv_file_train = "https://storage.googleapis.com/ml-service-repository-datastorage/Classification_of_clothing_through_images_fashion-mnist_train.csv"
-csv_file_test = "https://storage.googleapis.com/ml-service-repository-datastorage/Classification_of_clothing_through_images_fashion-mnist_test.csv"
-df_train = pd.read_csv(csv_file_train)
-df_test = pd.read_csv(csv_file_test)
-df_train.to_csv('df_train.csv', index=False)
-df_test.to_csv('df_test.csv', index=False)
-
-
-
-
-
-df_train.head()
-
-
-
-
-
-df_train.describe()
-
-
-df_test.describe()
-
-
-
-
-
-
-
-
-class_names = ['Top','Trouser','Pullover','Dress','Coat',
- 'Sandal','Shirt','Sneaker','Bag','Ankle boot']
-
-
-
-
-
-df_train, df_val = train_test_split(df_train, test_size=0.1, random_state=365)
-print(f"{len(df_train)} train examples")
-print(f"{len(df_val)} validation examples")
-print(f"{len(df_test)} test examples")
-
-
-
-
-
-def get_classes_distribution(data):
- # Get the count for each label
- label_counts = data["label"].value_counts()
-
- # Get total number of samples
- total_samples = len(data)
-
-
- # Count the number of items in each class
- for i in range(len(label_counts)):
- label = class_names[label_counts.index[i]]
- count = label_counts.values[i]
- percent = (count / total_samples) * 100
- print("{:<20s}: {} or {}%".format(label, count, percent))
-
-print("\nTRAIN DISTRIBUTION\n")
-get_classes_distribution(df_train)
-print("\nVALIDATION DISTRIBUTION\n")
-get_classes_distribution(df_val)
-print("\nTEST DISTRIBUTION\n")
-get_classes_distribution(df_test)
-
-
-
-
-
-def func(pct, allvalues):
- absolute = int(pct / 100.*np.sum(allvalues))
- return "{:.1f}%\n({:d})".format(pct, absolute)
-
-def plot_pie(title, data):
- # Creating plot
- fig, ax = plt.subplots(figsize =(10, 7))
- plt.pie(data, autopct = lambda pct: func(pct, data), labels = class_names)
- ax.set_title(title)
-
- # show plot
- plt.show()
-
-plot_pie("Train data distribution", df_train["label"].value_counts())
-plot_pie("Validation data distribution", df_val["label"].value_counts())
-plot_pie("Test data distribution", df_test["label"].value_counts())
-
-
-
-
-
-# Make copies of the data to allow easy exploration
-df_train_exp_copy = df_train.copy()
-y_train_exp = df_train_exp_copy.pop('label').to_numpy()
-x_train_exp = df_train_exp_copy.to_numpy()
-
-
-# Take a single image, and remove the color dimension by reshaping
-image = x_train_exp[0].reshape((28,28)) / 255.0
-
-plt.figure()
-plt.imshow(image, cmap=plt.cm.binary)
-plt.colorbar()
-plt.grid(False)
-plt.show()
-
-
-
-
-
-plt.figure(figsize=(10,10))
-i = 0
-for index in range(len(x_train_exp)):
- label = y_train_exp[index]
- image = x_train_exp[index] / 255.0
- if label == i:
- image = image.reshape((28,28))
- plt.subplot(5,5,i+1)
- plt.xticks([])
- plt.yticks([])
- plt.grid(False)
- plt.imshow(image, cmap=plt.cm.binary)
- plt.title(class_names[label])
- i += 1
- if i == 10:
- break
-plt.show()
-
-
-
-
-
-
-
-
-
-
-
-df_train_copy = df_train.copy()
-y_train = df_train_copy.pop('label').to_numpy()
-x_train = df_train_copy.to_numpy()
-df_val_copy = df_val.copy()
-y_val = df_val_copy.pop('label').to_numpy()
-x_val = df_val_copy.to_numpy()
-df_test_copy = df_test.copy()
-y_test = df_test_copy.pop('label').to_numpy()
-x_test = df_test_copy.to_numpy()
-
-
-
-
-
-x_train = x_train / 255.0
-x_val = x_val / 255.0
-x_test = x_test / 255.0
-
-
-
-
-
-IMG_ROWS = 28
-IMG_COLS = 28
-IMAGE_SHAPE = (IMG_ROWS, IMG_COLS, 1)
-x_train = x_train.reshape(x_train.shape[0], *IMAGE_SHAPE)
-x_val = x_val.reshape(x_val.shape[0], *IMAGE_SHAPE)
-x_test = x_test.reshape(x_test.shape[0], *IMAGE_SHAPE)
-
-
-
-
-
-y_train = to_categorical(y_train, 10)
-y_val = to_categorical(y_val, 10)
-y_test = to_categorical(y_test, 10)
-
-
-
-
-
-print(x_train.shape)
-print(y_train.shape)
-
-print(x_val.shape)
-print(y_val.shape)
-
-print(x_test.shape)
-print(y_test.shape)
-
-
-
-
-
-
-
-
-
-model = Sequential()
-model.add(Conv2D(filters=32,kernel_size=3, activation='relu', padding='same', input_shape=(28, 28,1)))
-model.add(Conv2D(filters=32,kernel_size=3,padding='same', activation='relu'))
-model.add(MaxPooling2D(pool_size=(2, 2)))
-model.add(Flatten())
-model.add(Dropout(0.40))
-model.add(Dense(units=128, activation='relu'))
-model.add(Dense(units=10, activation='softmax'))
-model.summary()
-
-
-
-
-
-model.compile(
- optimizer=tf.keras.optimizers.Adam(),
- loss= tf.keras.losses.categorical_crossentropy,
- metrics=['accuracy']
-)
-
-
-
-
-
-# Determine the maximum number of epochs
-NUM_EPOCHS = 10
-BATCH_SIZE = 64
-
-# Fit the model,
-# specify the training data
-# the total number of epochs
-# and the validation data we just created
-history = model.fit(
- x_train,
- y_train,
- batch_size=BATCH_SIZE,
- epochs=NUM_EPOCHS,
- validation_data=(x_val, y_val),
- validation_steps=10,
- verbose =2
-)
-
-
-
-
-
-acc = history.history["accuracy"]
-val_acc = history.history["val_accuracy"]
-loss = history.history["loss"]
-val_loss = history.history["val_loss"]
-
-epochs = range(len(acc))
-
-plt.plot(epochs, acc, "darkgreen", label="Training accuracy")
-plt.plot(epochs, val_acc, "darkblue", label="Validation accuracy")
-plt.plot(epochs, loss, "lightgreen", label="Training loss")
-plt.plot(epochs, val_loss, "lightblue", label="Validation loss")
-plt.title("Training and validation accuracy")
-plt.xlabel("Epochs")
-plt.ylabel("Precent/100")
-plt.legend(loc=0)
-plt.figure()
-
-
-plt.show()
-
-
-
-
-
-
-
-
-test_loss, test_accuracy = model.evaluate(x_test, y_test, verbose=0)
-
-
-print('Test loss: {0:.2f}. Test accuracy: {1:.2f}%'.format(test_loss, test_accuracy*100.))
-
-
-
-
-
-
-
-
-predicted_classes = (model.predict(x_test) > 0.5).astype("int32")
-
-
-print(classification_report(y_test, predicted_classes, target_names=class_names))
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/Marketing/.DS_Store b/Marketing/.DS_Store
deleted file mode 100644
index 2772d71e0ccc56c37b0aa20f5aa4faa34271c84a..0000000000000000000000000000000000000000
Binary files a/Marketing/.DS_Store and /dev/null differ
diff --git a/Marketing/Generation of Individual Playlists/.DS_Store b/Marketing/Generation of Individual Playlists/.DS_Store
deleted file mode 100644
index aa09114747f3228b10aa262b5af0148f8b22c2db..0000000000000000000000000000000000000000
Binary files a/Marketing/Generation of Individual Playlists/.DS_Store and /dev/null differ
diff --git a/Marketing/Generation of Individual Playlists/.virtual_documents/notebook.ipynb b/Marketing/Generation of Individual Playlists/.virtual_documents/notebook.ipynb
deleted file mode 100644
index f9076da0fae11136ad852cc4dbdd36d4ac53c1f4..0000000000000000000000000000000000000000
--- a/Marketing/Generation of Individual Playlists/.virtual_documents/notebook.ipynb
+++ /dev/null
@@ -1,339 +0,0 @@
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-pip install tensorflow-datasets
-
-
-import numpy as np
-import pandas as pd
-import matplotlib.pyplot as plt
-import tensorflow as tf
-
-import tensorflow_datasets as tfds
-from tensorflow.keras.models import Sequential
-from tensorflow.keras.layers import Flatten, Dense, Conv2D, MaxPooling2D, Dropout, Layer
-from tensorflow.keras.utils import to_categorical, plot_model
-from sklearn.model_selection import train_test_split
-from sklearn.metrics import classification_report
-
-
-# Should be 2.5.0
-tf.__version__
-
-
-
-
-
-
-
-
-csv_file_train = "https://storage.googleapis.com/ml-service-repository-datastorage/Classification_of_clothing_through_images_fashion-mnist_train.csv"
-csv_file_test = "https://storage.googleapis.com/ml-service-repository-datastorage/Classification_of_clothing_through_images_fashion-mnist_test.csv"
-df_train = pd.read_csv(csv_file_train)
-df_test = pd.read_csv(csv_file_test)
-df_train.to_csv('df_train.csv', index=False)
-df_test.to_csv('df_test.csv', index=False)
-
-
-
-
-
-df_train.head()
-
-
-
-
-
-df_train.describe()
-
-
-df_test.describe()
-
-
-
-
-
-
-
-
-class_names = ['Top','Trouser','Pullover','Dress','Coat',
- 'Sandal','Shirt','Sneaker','Bag','Ankle boot']
-
-
-
-
-
-df_train, df_val = train_test_split(df_train, test_size=0.1, random_state=365)
-print(f"{len(df_train)} train examples")
-print(f"{len(df_val)} validation examples")
-print(f"{len(df_test)} test examples")
-
-
-
-
-
-def get_classes_distribution(data):
- # Get the count for each label
- label_counts = data["label"].value_counts()
-
- # Get total number of samples
- total_samples = len(data)
-
-
- # Count the number of items in each class
- for i in range(len(label_counts)):
- label = class_names[label_counts.index[i]]
- count = label_counts.values[i]
- percent = (count / total_samples) * 100
- print("{:<20s}: {} or {}%".format(label, count, percent))
-
-print("\nTRAIN DISTRIBUTION\n")
-get_classes_distribution(df_train)
-print("\nVALIDATION DISTRIBUTION\n")
-get_classes_distribution(df_val)
-print("\nTEST DISTRIBUTION\n")
-get_classes_distribution(df_test)
-
-
-
-
-
-def func(pct, allvalues):
- absolute = int(pct / 100.*np.sum(allvalues))
- return "{:.1f}%\n({:d})".format(pct, absolute)
-
-def plot_pie(title, data):
- # Creating plot
- fig, ax = plt.subplots(figsize =(10, 7))
- plt.pie(data, autopct = lambda pct: func(pct, data), labels = class_names)
- ax.set_title(title)
-
- # show plot
- plt.show()
-
-plot_pie("Train data distribution", df_train["label"].value_counts())
-plot_pie("Validation data distribution", df_val["label"].value_counts())
-plot_pie("Test data distribution", df_test["label"].value_counts())
-
-
-
-
-
-# Make copies of the data to allow easy exploration
-df_train_exp_copy = df_train.copy()
-y_train_exp = df_train_exp_copy.pop('label').to_numpy()
-x_train_exp = df_train_exp_copy.to_numpy()
-
-
-# Take a single image, and remove the color dimension by reshaping
-image = x_train_exp[0].reshape((28,28)) / 255.0
-
-plt.figure()
-plt.imshow(image, cmap=plt.cm.binary)
-plt.colorbar()
-plt.grid(False)
-plt.show()
-
-
-
-
-
-plt.figure(figsize=(10,10))
-i = 0
-for index in range(len(x_train_exp)):
- label = y_train_exp[index]
- image = x_train_exp[index] / 255.0
- if label == i:
- image = image.reshape((28,28))
- plt.subplot(5,5,i+1)
- plt.xticks([])
- plt.yticks([])
- plt.grid(False)
- plt.imshow(image, cmap=plt.cm.binary)
- plt.title(class_names[label])
- i += 1
- if i == 10:
- break
-plt.show()
-
-
-
-
-
-
-
-
-
-
-
-df_train_copy = df_train.copy()
-y_train = df_train_copy.pop('label').to_numpy()
-x_train = df_train_copy.to_numpy()
-df_val_copy = df_val.copy()
-y_val = df_val_copy.pop('label').to_numpy()
-x_val = df_val_copy.to_numpy()
-df_test_copy = df_test.copy()
-y_test = df_test_copy.pop('label').to_numpy()
-x_test = df_test_copy.to_numpy()
-
-
-
-
-
-x_train = x_train / 255.0
-x_val = x_val / 255.0
-x_test = x_test / 255.0
-
-
-
-
-
-IMG_ROWS = 28
-IMG_COLS = 28
-IMAGE_SHAPE = (IMG_ROWS, IMG_COLS, 1)
-x_train = x_train.reshape(x_train.shape[0], *IMAGE_SHAPE)
-x_val = x_val.reshape(x_val.shape[0], *IMAGE_SHAPE)
-x_test = x_test.reshape(x_test.shape[0], *IMAGE_SHAPE)
-
-
-
-
-
-y_train = to_categorical(y_train, 10)
-y_val = to_categorical(y_val, 10)
-y_test = to_categorical(y_test, 10)
-
-
-
-
-
-print(x_train.shape)
-print(y_train.shape)
-
-print(x_val.shape)
-print(y_val.shape)
-
-print(x_test.shape)
-print(y_test.shape)
-
-
-
-
-
-
-
-
-
-model = Sequential()
-model.add(Conv2D(filters=32,kernel_size=3, activation='relu', padding='same', input_shape=(28, 28,1)))
-model.add(Conv2D(filters=32,kernel_size=3,padding='same', activation='relu'))
-model.add(MaxPooling2D(pool_size=(2, 2)))
-model.add(Flatten())
-model.add(Dropout(0.40))
-model.add(Dense(units=128, activation='relu'))
-model.add(Dense(units=10, activation='softmax'))
-model.summary()
-
-
-
-
-
-model.compile(
- optimizer=tf.keras.optimizers.Adam(),
- loss= tf.keras.losses.categorical_crossentropy,
- metrics=['accuracy']
-)
-
-
-
-
-
-# Determine the maximum number of epochs
-NUM_EPOCHS = 10
-BATCH_SIZE = 64
-
-# Fit the model,
-# specify the training data
-# the total number of epochs
-# and the validation data we just created
-history = model.fit(
- x_train,
- y_train,
- batch_size=BATCH_SIZE,
- epochs=NUM_EPOCHS,
- validation_data=(x_val, y_val),
- validation_steps=10,
- verbose =2
-)
-
-
-
-
-
-acc = history.history["accuracy"]
-val_acc = history.history["val_accuracy"]
-loss = history.history["loss"]
-val_loss = history.history["val_loss"]
-
-epochs = range(len(acc))
-
-plt.plot(epochs, acc, "darkgreen", label="Training accuracy")
-plt.plot(epochs, val_acc, "darkblue", label="Validation accuracy")
-plt.plot(epochs, loss, "lightgreen", label="Training loss")
-plt.plot(epochs, val_loss, "lightblue", label="Validation loss")
-plt.title("Training and validation accuracy")
-plt.xlabel("Epochs")
-plt.ylabel("Precent/100")
-plt.legend(loc=0)
-plt.figure()
-
-
-plt.show()
-
-
-
-
-
-
-
-
-test_loss, test_accuracy = model.evaluate(x_test, y_test, verbose=0)
-
-
-print('Test loss: {0:.2f}. Test accuracy: {1:.2f}%'.format(test_loss, test_accuracy*100.))
-
-
-
-
-
-
-
-
-predicted_classes = (model.predict(x_test) > 0.5).astype("int32")
-
-
-print(classification_report(y_test, predicted_classes, target_names=class_names))
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/Marketing/Generation of Individual Playlists/notebook.ipynb b/Marketing/Generation of Individual Playlists/notebook.ipynb
index 62c5c16efbc2d603564b25f6f2fb163c17225f1c..426699106c7f6dcdb99c53044574ab293a2b41ba 100644
--- a/Marketing/Generation of Individual Playlists/notebook.ipynb
+++ b/Marketing/Generation of Individual Playlists/notebook.ipynb
@@ -18,7 +18,7 @@
"metadata": {
"editable": true,
"include": true,
- "paragraph": "business",
+ "paragraph": "Business",
"slideshow": {
"slide_type": ""
},
@@ -27,18 +27,16 @@
]
},
"source": [
- "Spotify, ein führender Konzern in der Musikindustrie, möchte die Möglichkeiten und Herausforderungen der Künstlichen Intelligenz (KI) nutzen, um für jeden Benutzer eine einzigartige und personalisierte Wiedergabeliste zu erstellen. Durch den Einsatz von maschinellem Lernen und Algorithmen soll eine Lösung entwickelt werden, die sich individuell an die Vorlieben und das Verhalten der Nutzer anpasst. Ziel ist es, durch diese innovative Anwendung die Nutzerbindung zu stärken und das Hörerlebnis weiter zu optimieren. Spotify, ein führender Konzern in der Musikindustrie, möchte die Möglichkeiten und Herausforderungen der Künstlichen Intelligenz (KI) nutzen, um für jeden Benutzer eine einzigartige und personalisierte Wiedergabeliste zu erstellen. Durch den Einsatz von maschinellem Lernen und Algorithmen soll eine Lösung entwickelt werden, die sich individuell an die Vorlieben und das Verhalten der Nutzer anpasst. Ziel ist es, durch diese innovative Anwendung die Nutzerbindung zu stärken und das Hörerlebnis weiter zu optimieren. "
+ "Viele Online-Versandhandelsunternehmen haben eine hohe Rücksendequote (von bis zu 50%), wobei 97% aller zurückgesendeten Produkte wieder auf Lager genommen und verkauft werden können. Um die Waren wieder zu verkaufen, müssen sie entsprechend identifiziert, etikettiert und wieder eingelagert werden.\n",
+ "\n",
+ "Angenommen, dass im Jahr 2020 185,5 Millionen Bestellungen (Statista, 2021) mit jeweils 6 Artikeln (Annahme) eingehen würden, dann würde eine Rücksendequote von 50% bedeuten, dass 556,5 Millionen Artikel neu identifiziert und kategorisiert werden müssten.\n",
+ "\n",
+ "Um diesen Prozess zu unterstützen und die Identifizierung der zurückgesendeten Kleidungsstücke zu erleichtern, soll eine Bilderkennungssoftware entwickelt werden, die die zugehörigen Kategorien der einzelnen Kleidungsstücke anhand von Bildern erkennt."
]
},
{
"cell_type": "markdown",
- "metadata": {
- "editable": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
+ "metadata": {},
"source": [
"## 2. Data Understanding"
]
@@ -48,7 +46,7 @@
"metadata": {
"editable": true,
"include": true,
- "paragraph": "daten",
+ "paragraph": "Daten",
"slideshow": {
"slide_type": ""
},
@@ -57,143 +55,151 @@
]
},
"source": [
- "Für das Projekt zur Generierung personalisierter Wiedergabelisten wird der Datensatz \"Spotify Dataset 1921-2020 (160k+ Tracks)\" verwendet, der auf der Plattform Kaggle veröffentlicht wurde. Dieser Datensatz, erstellt von Spotify und im Jahr 2020 veröffentlicht, liegt im CSV-Format vor und enthält eine Vielzahl von Informationen über Songs. Der Datensatz umfasst insgesamt 19 Attribute, die verschiedene Aspekte von Songs beschreiben, darunter Komponenten wie Erscheinungsjahr, Popularität, Dauer, Tanzbarkeit und viele mehr.\n",
+ "### Data Understanding\n",
"\n",
- "Die Zielvariable dieses Datensatzes ist die Popularität eines Songs, gemessen als Ganzzahl (integer). Die Attribute umfassen eine Mischung aus ganzzahligen Werten (z.B. Jahr, Dauer, Schlüssel, Modus), Gleitkommazahlen (z.B. Tanzbarkeit, Energie, Lautstärke, Sprachverständlichkeit) und kategorialen Merkmalen (z.B. Explicit Content, Taktart). Um ein besseres Verständnis für den Datensatz zu entwickeln, werden auch Standortparameter wie Mittelwert und Median sowie Verteilungsparameter wie Standardabweichung und Quartile der numerischen Attribute berechnet. Eine Korrelationsanalyse mittels Pearson-Korrelation wird durchgeführt, um die Beziehungen zwischen den numerischen Attributen und der Zielvariable Popularität zu identifizieren."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "source": [
- "# 3. Datenaufbereitung"
+ "Der Datenrahmen **fashion-mnist_test** stammt von Kaggle und ist über das [Fashion-MNIST GitHub Repository](https://github.com/zalandoresearch/fashion-mnist) zugänglich. Dieser Datensatz wurde 2017 von Zalando erstellt und besteht aus Artikelbildern des Unternehmens.\n",
+ "\n",
+ "Der Datensatz liegt im CSV-Format vor und enthält insgesamt 70.000 Bilder von Kleidungsstücken, die in 60.000 Trainingsbilder und 10.000 Testbilder unterteilt sind. Jedes Bild wurde auf eine Größe von 28x28 Pixel skaliert und in Graustufen umgewandelt.\n",
+ "\n",
+ "Der Datensatz umfasst 784 Merkmale, die jeweils einem Pixel des Bildes entsprechen, sowie ein zusätzliches Label, das die Kategorie des Kleidungsstücks angibt. Sowohl die Merkmale als auch die Labels sind als Integer-Werte gespeichert. Die Pixelwerte repräsentieren die Intensität des Grautons, während die Labels die verschiedenen Kleidungsstückkategorien darstellen.\n",
+ "\n",
+ "Insgesamt enthält der Datensatz 70.000 Beobachtungen. Die Parameter \"Standort\", Verteilungsparameter und Korrelationsanalyse sind für diesen Datensatz nicht anwendbar.\n",
+ "\n",
+ "Der Fashion-MNIST-Datensatz bietet eine moderne und herausfordernde Alternative zum klassischen MNIST-Datensatz, da er realistischere und komplexere Bilder von Kleidungsstücken enthält. Dies stellt eine größere Herausforderung für Bildklassifizierungsmodelle dar und bietet eine realistischere Anwendungsmöglichkeit im Bereich der maschinellen Bildverarbeitung."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "Der Python-Code dient der Datenaufbereitung, indem er kategorische Variablen in binäre Dummy-Variablen umwandelt. Dies wird mithilfe der Funktion `pd.get_dummies` erreicht, die die kategorischen Daten in eine Serie von 0-1 kodierten Variablen konvertiert. Der Parameter `drop_first=True` sorgt dafür, dass die erste Kategorie jeder Variablen ausgelassen wird, um Multikollinearität zu vermeiden. Anschließend wird der resultierende DataFrame angezeigt, der nur numerische Spalten enthält, was für maschinelle Lernmodelle geeignet ist. Die ersten fünf Zeilen des DataFrames zeigen Beispiele der umgewandelten Daten mit verschiedenen numerischen Attributen wie `valence`, `acousticness`, `danceability`, `duration_ms`, `explicit`, `instrumentalness`, `key`, `liveness`, `loudness`, `mode`, `popularity`, `speechiness` und `tempo`."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "source": [
- "## 4. Datenmodell"
+ "### 2.1. Import von relevanten Modulen"
]
},
{
- "cell_type": "markdown",
+ "cell_type": "code",
+ "execution_count": 1,
"metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Requirement already satisfied: tensorflow-datasets in c:\\users\\ar\\anaconda3\\lib\\site-packages (4.9.6)\n",
+ "Requirement already satisfied: absl-py in c:\\users\\ar\\anaconda3\\lib\\site-packages (from tensorflow-datasets) (2.1.0)\n",
+ "Requirement already satisfied: click in c:\\users\\ar\\anaconda3\\lib\\site-packages (from tensorflow-datasets) (8.0.4)\n",
+ "Requirement already satisfied: dm-tree in c:\\users\\ar\\anaconda3\\lib\\site-packages (from tensorflow-datasets) (0.1.8)\n",
+ "Requirement already satisfied: immutabledict in c:\\users\\ar\\anaconda3\\lib\\site-packages (from tensorflow-datasets) (4.2.0)\n",
+ "Requirement already satisfied: numpy in c:\\users\\ar\\anaconda3\\lib\\site-packages (from tensorflow-datasets) (1.24.3)\n",
+ "Requirement already satisfied: promise in c:\\users\\ar\\anaconda3\\lib\\site-packages (from tensorflow-datasets) (2.3)\n",
+ "Requirement already satisfied: protobuf>=3.20 in c:\\users\\ar\\anaconda3\\lib\\site-packages (from tensorflow-datasets) (4.25.3)\n",
+ "Requirement already satisfied: psutil in c:\\users\\ar\\anaconda3\\lib\\site-packages (from tensorflow-datasets) (5.9.0)\n",
+ "Requirement already satisfied: pyarrow in c:\\users\\ar\\anaconda3\\lib\\site-packages (from tensorflow-datasets) (11.0.0)\n",
+ "Requirement already satisfied: requests>=2.19.0 in c:\\users\\ar\\anaconda3\\lib\\site-packages (from tensorflow-datasets) (2.31.0)\n",
+ "Requirement already satisfied: simple-parsing in c:\\users\\ar\\anaconda3\\lib\\site-packages (from tensorflow-datasets) (0.1.5)\n",
+ "Requirement already satisfied: tensorflow-metadata in c:\\users\\ar\\anaconda3\\lib\\site-packages (from tensorflow-datasets) (1.15.0)\n",
+ "Requirement already satisfied: termcolor in c:\\users\\ar\\anaconda3\\lib\\site-packages (from tensorflow-datasets) (2.4.0)\n",
+ "Requirement already satisfied: toml in c:\\users\\ar\\anaconda3\\lib\\site-packages (from tensorflow-datasets) (0.10.2)\n",
+ "Requirement already satisfied: tqdm in c:\\users\\ar\\anaconda3\\lib\\site-packages (from tensorflow-datasets) (4.65.0)\n",
+ "Requirement already satisfied: wrapt in c:\\users\\ar\\anaconda3\\lib\\site-packages (from tensorflow-datasets) (1.14.1)\n",
+ "Requirement already satisfied: etils>=1.9.1 in c:\\users\\ar\\anaconda3\\lib\\site-packages (from etils[enp,epath,epy,etree]>=1.9.1; python_version >= \"3.11\"->tensorflow-datasets) (1.9.2)\n",
+ "Requirement already satisfied: fsspec in c:\\users\\ar\\anaconda3\\lib\\site-packages (from etils[enp,epath,epy,etree]>=1.9.1; python_version >= \"3.11\"->tensorflow-datasets) (2023.4.0)\n",
+ "Requirement already satisfied: importlib_resources in c:\\users\\ar\\anaconda3\\lib\\site-packages (from etils[enp,epath,epy,etree]>=1.9.1; python_version >= \"3.11\"->tensorflow-datasets) (6.4.0)\n",
+ "Requirement already satisfied: typing_extensions in c:\\users\\ar\\anaconda3\\lib\\site-packages (from etils[enp,epath,epy,etree]>=1.9.1; python_version >= \"3.11\"->tensorflow-datasets) (4.7.1)\n",
+ "Requirement already satisfied: zipp in c:\\users\\ar\\anaconda3\\lib\\site-packages (from etils[enp,epath,epy,etree]>=1.9.1; python_version >= \"3.11\"->tensorflow-datasets) (3.11.0)\n",
+ "Requirement already satisfied: charset-normalizer<4,>=2 in c:\\users\\ar\\anaconda3\\lib\\site-packages (from requests>=2.19.0->tensorflow-datasets) (2.0.4)\n",
+ "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\ar\\anaconda3\\lib\\site-packages (from requests>=2.19.0->tensorflow-datasets) (3.4)\n",
+ "Requirement already satisfied: urllib3<3,>=1.21.1 in c:\\users\\ar\\anaconda3\\lib\\site-packages (from requests>=2.19.0->tensorflow-datasets) (1.26.16)\n",
+ "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\ar\\anaconda3\\lib\\site-packages (from requests>=2.19.0->tensorflow-datasets) (2024.2.2)\n",
+ "Requirement already satisfied: colorama in c:\\users\\ar\\anaconda3\\lib\\site-packages (from click->tensorflow-datasets) (0.4.6)\n",
+ "Requirement already satisfied: six in c:\\users\\ar\\anaconda3\\lib\\site-packages (from promise->tensorflow-datasets) (1.16.0)\n",
+ "Requirement already satisfied: docstring-parser~=0.15 in c:\\users\\ar\\anaconda3\\lib\\site-packages (from simple-parsing->tensorflow-datasets) (0.16)\n",
+ "Requirement already satisfied: googleapis-common-protos<2,>=1.56.4 in c:\\users\\ar\\anaconda3\\lib\\site-packages (from tensorflow-metadata->tensorflow-datasets) (1.63.1)\n",
+ "Note: you may need to restart the kernel to use updated packages.\n"
+ ]
+ }
+ ],
"source": [
- "Zur Modellierung der Daten wird die Lineare Regression als Algorithmus gewählt. Dieser Ansatz ermöglicht es, die Beziehung zwischen den Attributen und der Zielvariable Popularität zu modellieren und Vorhersagen zu treffen."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "source": [
- "## 5. Evaluation"
+ "pip install tensorflow-datasets"
]
},
{
- "cell_type": "markdown",
+ "cell_type": "code",
+ "execution_count": 2,
"metadata": {},
+ "outputs": [],
"source": [
- "Das Modell wird im Rahmen des überwachtes Lernens (supervised learning) entwickelt, wobei sowohl Klassifikations- als auch Regressionsansätze in Betracht gezogen werden. Der Datensatz wird in Trainings- und Testdaten aufgeteilt, wobei 80% der Daten für das Training und 20% für die Evaluierung des Modells verwendet werden.\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "import tensorflow as tf\n",
"\n",
- "Die Leistungsbewertung des Modells erfolgt anhand spezifischer Metriken. Die Trainingsperformance des Modells liegt bei 0.42002501021526495, während die Testperformance bei 0.4207911441145905 liegt. Diese Metriken geben Aufschluss über die Genauigkeit und Verlässlichkeit des Modells, wobei diese hier beide mit ca. 42% nicht optimal sind und durch weitere Trainingszyklen verbessert werden sollten."
+ "import tensorflow_datasets as tfds\n",
+ "from tensorflow.keras.models import Sequential\n",
+ "from tensorflow.keras.layers import Flatten, Dense, Conv2D, MaxPooling2D, Dropout, Layer\n",
+ "from tensorflow.keras.utils import to_categorical, plot_model\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "from sklearn.metrics import classification_report"
]
},
{
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'2.16.1'"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
- "## 6. Umsetzung"
+ "# Should be 2.5.0\n",
+ "tf.__version__"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "Die entwickelte Lösung zielt darauf ab, einen Service zur Generierung individueller Wiedergabelisten bereitzustellen. Die Hauptzielgruppe sind die Spotify-Nutzer, die von personalisierten, auf ihre Präferenzen abgestimmten Playlists profitieren sollen. Dieser Service hat das Potenzial, die Nutzerbindung zu erhöhen und das Kundenerlebnis deutlich zu verbessern."
+ "### 2.2 Daten einlesen\n"
]
},
{
- "attachments": {},
"cell_type": "markdown",
- "metadata": {
- "collapsed": true,
- "jupyter": {
- "outputs_hidden": true
- }
- },
- "source": [
- "## 2.1 Import von relevanten Modulen"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
"metadata": {},
- "outputs": [],
"source": [
- "# @hidden_cell\n",
- "# The project token is an authorization token that is used to access project resources like data sources, connections, and used by platform APIs.\n",
- "# from project_lib import Project\n",
- "# project = Project(project_id='148a63b8-8cd0-4ae9-acd7-382d6f8da2d1', project_access_token='p-b4ba4eff97677a6d061b1f9eb562ffce54a4e527')\n",
- "# pc = project.project_context"
+ "Die Trainings- und Testdaten sind bereits aufgeteilt in zwei verschiedene CSV Dateien"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
- "import numpy as np\n",
- "import pandas as pd\n",
- "import matplotlib.pyplot as plt\n",
- "import seaborn as sns\n",
- "sns.set()"
+ "csv_file_train = \"https://storage.googleapis.com/ml-service-repository-datastorage/Classification_of_clothing_through_images_fashion-mnist_train.csv\"\n",
+ "csv_file_test = \"https://storage.googleapis.com/ml-service-repository-datastorage/Classification_of_clothing_through_images_fashion-mnist_test.csv\"\n",
+ "df_train = pd.read_csv(csv_file_train) \n",
+ "df_test = pd.read_csv(csv_file_test)\n",
+ "df_train.to_csv('df_train.csv', index=False)\n",
+ "df_test.to_csv('df_test.csv', index=False)"
]
},
{
- "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 2.2 Daten einlesen"
+ "## 2.3. Data Analysis"
]
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 5,
"metadata": {},
"outputs": [
{
@@ -217,216 +223,204 @@
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
- " <th>valence</th>\n",
- " <th>year</th>\n",
- " <th>acousticness</th>\n",
- " <th>artists</th>\n",
- " <th>danceability</th>\n",
- " <th>duration_ms</th>\n",
- " <th>energy</th>\n",
- " <th>explicit</th>\n",
- " <th>id</th>\n",
- " <th>instrumentalness</th>\n",
- " <th>key</th>\n",
- " <th>liveness</th>\n",
- " <th>loudness</th>\n",
- " <th>mode</th>\n",
- " <th>name</th>\n",
- " <th>popularity</th>\n",
- " <th>release_date</th>\n",
- " <th>speechiness</th>\n",
- " <th>tempo</th>\n",
+ " <th>label</th>\n",
+ " <th>pixel1</th>\n",
+ " <th>pixel2</th>\n",
+ " <th>pixel3</th>\n",
+ " <th>pixel4</th>\n",
+ " <th>pixel5</th>\n",
+ " <th>pixel6</th>\n",
+ " <th>pixel7</th>\n",
+ " <th>pixel8</th>\n",
+ " <th>pixel9</th>\n",
+ " <th>...</th>\n",
+ " <th>pixel775</th>\n",
+ " <th>pixel776</th>\n",
+ " <th>pixel777</th>\n",
+ " <th>pixel778</th>\n",
+ " <th>pixel779</th>\n",
+ " <th>pixel780</th>\n",
+ " <th>pixel781</th>\n",
+ " <th>pixel782</th>\n",
+ " <th>pixel783</th>\n",
+ " <th>pixel784</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
- " <td>0.0594</td>\n",
- " <td>1921</td>\n",
- " <td>0.982</td>\n",
- " <td>['Sergei Rachmaninoff', 'James Levine', 'Berli...</td>\n",
- " <td>0.279</td>\n",
- " <td>831667</td>\n",
- " <td>0.211</td>\n",
- " <td>0</td>\n",
- " <td>4BJqT0PrAfrxzMOxytFOIz</td>\n",
- " <td>0.878000</td>\n",
- " <td>10</td>\n",
- " <td>0.665</td>\n",
- " <td>-20.096</td>\n",
- " <td>1</td>\n",
- " <td>Piano Concerto No. 3 in D Minor, Op. 30: III. ...</td>\n",
- " <td>4</td>\n",
- " <td>1921</td>\n",
- " <td>0.0366</td>\n",
- " <td>80.954</td>\n",
+ " <td>2</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>...</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
- " <td>0.9630</td>\n",
- " <td>1921</td>\n",
- " <td>0.732</td>\n",
- " <td>['Dennis Day']</td>\n",
- " <td>0.819</td>\n",
- " <td>180533</td>\n",
- " <td>0.341</td>\n",
- " <td>0</td>\n",
- " <td>7xPhfUan2yNtyFG0cUWkt8</td>\n",
- " <td>0.000000</td>\n",
- " <td>7</td>\n",
- " <td>0.160</td>\n",
- " <td>-12.441</td>\n",
- " <td>1</td>\n",
- " <td>Clancy Lowered the Boom</td>\n",
- " <td>5</td>\n",
- " <td>1921</td>\n",
- " <td>0.4150</td>\n",
- " <td>60.936</td>\n",
+ " <td>9</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>...</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
- " <td>0.0394</td>\n",
- " <td>1921</td>\n",
- " <td>0.961</td>\n",
- " <td>['KHP Kridhamardawa Karaton Ngayogyakarta Hadi...</td>\n",
- " <td>0.328</td>\n",
- " <td>500062</td>\n",
- " <td>0.166</td>\n",
- " <td>0</td>\n",
- " <td>1o6I8BglA6ylDMrIELygv1</td>\n",
- " <td>0.913000</td>\n",
- " <td>3</td>\n",
- " <td>0.101</td>\n",
- " <td>-14.850</td>\n",
- " <td>1</td>\n",
- " <td>Gati Bali</td>\n",
+ " <td>6</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
" <td>5</td>\n",
- " <td>1921</td>\n",
- " <td>0.0339</td>\n",
- " <td>110.339</td>\n",
+ " <td>0</td>\n",
+ " <td>...</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>30</td>\n",
+ " <td>43</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
- " <td>0.1650</td>\n",
- " <td>1921</td>\n",
- " <td>0.967</td>\n",
- " <td>['Frank Parker']</td>\n",
- " <td>0.275</td>\n",
- " <td>210000</td>\n",
- " <td>0.309</td>\n",
- " <td>0</td>\n",
- " <td>3ftBPsC5vPBKxYSee08FDH</td>\n",
- " <td>0.000028</td>\n",
- " <td>5</td>\n",
- " <td>0.381</td>\n",
- " <td>-9.316</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
" <td>1</td>\n",
- " <td>Danny Boy</td>\n",
+ " <td>2</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>...</td>\n",
" <td>3</td>\n",
- " <td>1921</td>\n",
- " <td>0.0354</td>\n",
- " <td>100.109</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
- " <td>0.2530</td>\n",
- " <td>1921</td>\n",
- " <td>0.957</td>\n",
- " <td>['Phil Regan']</td>\n",
- " <td>0.418</td>\n",
- " <td>166693</td>\n",
- " <td>0.193</td>\n",
- " <td>0</td>\n",
- " <td>4d6HGyGT8e121BsdKmw9v6</td>\n",
- " <td>0.000002</td>\n",
" <td>3</td>\n",
- " <td>0.229</td>\n",
- " <td>-10.096</td>\n",
- " <td>1</td>\n",
- " <td>When Irish Eyes Are Smiling</td>\n",
- " <td>2</td>\n",
- " <td>1921</td>\n",
- " <td>0.0380</td>\n",
- " <td>101.665</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>...</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
+ "<p>5 rows × 785 columns</p>\n",
"</div>"
],
"text/plain": [
- " valence year acousticness \\\n",
- "0 0.0594 1921 0.982 \n",
- "1 0.9630 1921 0.732 \n",
- "2 0.0394 1921 0.961 \n",
- "3 0.1650 1921 0.967 \n",
- "4 0.2530 1921 0.957 \n",
+ " label pixel1 pixel2 pixel3 pixel4 pixel5 pixel6 pixel7 pixel8 \\\n",
+ "0 2 0 0 0 0 0 0 0 0 \n",
+ "1 9 0 0 0 0 0 0 0 0 \n",
+ "2 6 0 0 0 0 0 0 0 5 \n",
+ "3 0 0 0 0 1 2 0 0 0 \n",
+ "4 3 0 0 0 0 0 0 0 0 \n",
"\n",
- " artists danceability \\\n",
- "0 ['Sergei Rachmaninoff', 'James Levine', 'Berli... 0.279 \n",
- "1 ['Dennis Day'] 0.819 \n",
- "2 ['KHP Kridhamardawa Karaton Ngayogyakarta Hadi... 0.328 \n",
- "3 ['Frank Parker'] 0.275 \n",
- "4 ['Phil Regan'] 0.418 \n",
+ " pixel9 ... pixel775 pixel776 pixel777 pixel778 pixel779 pixel780 \\\n",
+ "0 0 ... 0 0 0 0 0 0 \n",
+ "1 0 ... 0 0 0 0 0 0 \n",
+ "2 0 ... 0 0 0 30 43 0 \n",
+ "3 0 ... 3 0 0 0 0 1 \n",
+ "4 0 ... 0 0 0 0 0 0 \n",
"\n",
- " duration_ms energy explicit id instrumentalness \\\n",
- "0 831667 0.211 0 4BJqT0PrAfrxzMOxytFOIz 0.878000 \n",
- "1 180533 0.341 0 7xPhfUan2yNtyFG0cUWkt8 0.000000 \n",
- "2 500062 0.166 0 1o6I8BglA6ylDMrIELygv1 0.913000 \n",
- "3 210000 0.309 0 3ftBPsC5vPBKxYSee08FDH 0.000028 \n",
- "4 166693 0.193 0 4d6HGyGT8e121BsdKmw9v6 0.000002 \n",
+ " pixel781 pixel782 pixel783 pixel784 \n",
+ "0 0 0 0 0 \n",
+ "1 0 0 0 0 \n",
+ "2 0 0 0 0 \n",
+ "3 0 0 0 0 \n",
+ "4 0 0 0 0 \n",
"\n",
- " key liveness loudness mode \\\n",
- "0 10 0.665 -20.096 1 \n",
- "1 7 0.160 -12.441 1 \n",
- "2 3 0.101 -14.850 1 \n",
- "3 5 0.381 -9.316 1 \n",
- "4 3 0.229 -10.096 1 \n",
- "\n",
- " name popularity release_date \\\n",
- "0 Piano Concerto No. 3 in D Minor, Op. 30: III. ... 4 1921 \n",
- "1 Clancy Lowered the Boom 5 1921 \n",
- "2 Gati Bali 5 1921 \n",
- "3 Danny Boy 3 1921 \n",
- "4 When Irish Eyes Are Smiling 2 1921 \n",
- "\n",
- " speechiness tempo \n",
- "0 0.0366 80.954 \n",
- "1 0.4150 60.936 \n",
- "2 0.0339 110.339 \n",
- "3 0.0354 100.109 \n",
- "4 0.0380 101.665 "
+ "[5 rows x 785 columns]"
]
},
- "execution_count": 10,
+ "execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "#Fetch the local file\n",
- "# my_file = project.get_file(\"data.csv\")\n",
- "\n",
- "#Read the CSV data file from the object storage into a pandas DataFrame\n",
- "#my_file.seek(0)\n",
- "\n",
- "#raw_data = pd.read_csv(my_file)\n",
- "raw_data = pd.read_csv(\"https://storage.googleapis.com/ml-service-repository-datastorage/Generation_of_Individual_Playlists_Generation-of-Individual-Playlists-data.csv\")\n",
- "\n",
- "raw_data.head()"
+ "df_train.head()"
]
},
{
- "cell_type": "code",
- "execution_count": 11,
+ "cell_type": "markdown",
"metadata": {},
- "outputs": [],
"source": [
- "#raw_data.head(5)"
+ "Das Beschreiben des Datenrahmens ist in diesem Fall nicht wirklich hilfreich, aber zeigt, dass die Daten nicht beschädigt sind, indem folgende Punkte überprüft werden:\n",
+ "\n",
+ "- Das Label muss zwischen 0 und 9 liegen.\n",
+ "- Die Pixelwerte müssen zwischen 0 und 255 (nicht negativ) liegen.\n",
+ "- Die Anzahl muss 60.000 (Training) und 10.000 (Test) betragen.\n",
+ "- Die maximale Anzahl an Pixeln muss für alle Zeilen 784 betragen."
]
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 6,
"metadata": {},
"outputs": [
{
@@ -450,362 +444,293 @@
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
- " <th>valence</th>\n",
- " <th>year</th>\n",
- " <th>acousticness</th>\n",
- " <th>artists</th>\n",
- " <th>danceability</th>\n",
- " <th>duration_ms</th>\n",
- " <th>energy</th>\n",
- " <th>explicit</th>\n",
- " <th>id</th>\n",
- " <th>instrumentalness</th>\n",
- " <th>key</th>\n",
- " <th>liveness</th>\n",
- " <th>loudness</th>\n",
- " <th>mode</th>\n",
- " <th>name</th>\n",
- " <th>popularity</th>\n",
- " <th>release_date</th>\n",
- " <th>speechiness</th>\n",
- " <th>tempo</th>\n",
+ " <th>label</th>\n",
+ " <th>pixel1</th>\n",
+ " <th>pixel2</th>\n",
+ " <th>pixel3</th>\n",
+ " <th>pixel4</th>\n",
+ " <th>pixel5</th>\n",
+ " <th>pixel6</th>\n",
+ " <th>pixel7</th>\n",
+ " <th>pixel8</th>\n",
+ " <th>pixel9</th>\n",
+ " <th>...</th>\n",
+ " <th>pixel775</th>\n",
+ " <th>pixel776</th>\n",
+ " <th>pixel777</th>\n",
+ " <th>pixel778</th>\n",
+ " <th>pixel779</th>\n",
+ " <th>pixel780</th>\n",
+ " <th>pixel781</th>\n",
+ " <th>pixel782</th>\n",
+ " <th>pixel783</th>\n",
+ " <th>pixel784</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
- " <td>170653.000000</td>\n",
- " <td>170653.000000</td>\n",
- " <td>170653.000000</td>\n",
- " <td>170653</td>\n",
- " <td>170653.000000</td>\n",
- " <td>1.706530e+05</td>\n",
- " <td>170653.000000</td>\n",
- " <td>170653.000000</td>\n",
- " <td>170653</td>\n",
- " <td>170653.000000</td>\n",
- " <td>170653.000000</td>\n",
- " <td>170653.000000</td>\n",
- " <td>170653.000000</td>\n",
- " <td>170653.000000</td>\n",
- " <td>170653</td>\n",
- " <td>170653.000000</td>\n",
- " <td>170653</td>\n",
- " <td>170653.000000</td>\n",
- " <td>170653.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>unique</th>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>34088</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>170653</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>133638</td>\n",
- " <td>NaN</td>\n",
- " <td>11244</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>top</th>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>['ÐрнеÑÑ‚ ХемингуÑй']</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>6Af5jOEKb2bef8XE74ptnw</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>White Christmas</td>\n",
- " <td>NaN</td>\n",
- " <td>1945</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>freq</th>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>1211</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>1</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>73</td>\n",
- " <td>NaN</td>\n",
- " <td>1446</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
+ " <td>60000.000000</td>\n",
+ " <td>60000.000000</td>\n",
+ " <td>60000.000000</td>\n",
+ " <td>60000.000000</td>\n",
+ " <td>60000.000000</td>\n",
+ " <td>60000.000000</td>\n",
+ " <td>60000.000000</td>\n",
+ " <td>60000.000000</td>\n",
+ " <td>60000.000000</td>\n",
+ " <td>60000.000000</td>\n",
+ " <td>...</td>\n",
+ " <td>60000.000000</td>\n",
+ " <td>60000.000000</td>\n",
+ " <td>60000.000000</td>\n",
+ " <td>60000.000000</td>\n",
+ " <td>60000.000000</td>\n",
+ " <td>60000.000000</td>\n",
+ " <td>60000.000000</td>\n",
+ " <td>60000.000000</td>\n",
+ " <td>60000.000000</td>\n",
+ " <td>60000.00000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
- " <td>0.528587</td>\n",
- " <td>1976.787241</td>\n",
- " <td>0.502115</td>\n",
- " <td>NaN</td>\n",
- " <td>0.537396</td>\n",
- " <td>2.309483e+05</td>\n",
- " <td>0.482389</td>\n",
- " <td>0.084575</td>\n",
- " <td>NaN</td>\n",
- " <td>0.167010</td>\n",
- " <td>5.199844</td>\n",
- " <td>0.205839</td>\n",
- " <td>-11.467990</td>\n",
- " <td>0.706902</td>\n",
- " <td>NaN</td>\n",
- " <td>31.431794</td>\n",
- " <td>NaN</td>\n",
- " <td>0.098393</td>\n",
- " <td>116.861590</td>\n",
+ " <td>4.500000</td>\n",
+ " <td>0.000900</td>\n",
+ " <td>0.006150</td>\n",
+ " <td>0.035333</td>\n",
+ " <td>0.101933</td>\n",
+ " <td>0.247967</td>\n",
+ " <td>0.411467</td>\n",
+ " <td>0.805767</td>\n",
+ " <td>2.198283</td>\n",
+ " <td>5.682000</td>\n",
+ " <td>...</td>\n",
+ " <td>34.625400</td>\n",
+ " <td>23.300683</td>\n",
+ " <td>16.588267</td>\n",
+ " <td>17.869433</td>\n",
+ " <td>22.814817</td>\n",
+ " <td>17.911483</td>\n",
+ " <td>8.520633</td>\n",
+ " <td>2.753300</td>\n",
+ " <td>0.855517</td>\n",
+ " <td>0.07025</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
- " <td>0.263171</td>\n",
- " <td>25.917853</td>\n",
- " <td>0.376032</td>\n",
- " <td>NaN</td>\n",
- " <td>0.176138</td>\n",
- " <td>1.261184e+05</td>\n",
- " <td>0.267646</td>\n",
- " <td>0.278249</td>\n",
- " <td>NaN</td>\n",
- " <td>0.313475</td>\n",
- " <td>3.515094</td>\n",
- " <td>0.174805</td>\n",
- " <td>5.697943</td>\n",
- " <td>0.455184</td>\n",
- " <td>NaN</td>\n",
- " <td>21.826615</td>\n",
- " <td>NaN</td>\n",
- " <td>0.162740</td>\n",
- " <td>30.708533</td>\n",
+ " <td>2.872305</td>\n",
+ " <td>0.094689</td>\n",
+ " <td>0.271011</td>\n",
+ " <td>1.222324</td>\n",
+ " <td>2.452871</td>\n",
+ " <td>4.306912</td>\n",
+ " <td>5.836188</td>\n",
+ " <td>8.215169</td>\n",
+ " <td>14.093378</td>\n",
+ " <td>23.819481</td>\n",
+ " <td>...</td>\n",
+ " <td>57.545242</td>\n",
+ " <td>48.854427</td>\n",
+ " <td>41.979611</td>\n",
+ " <td>43.966032</td>\n",
+ " <td>51.830477</td>\n",
+ " <td>45.149388</td>\n",
+ " <td>29.614859</td>\n",
+ " <td>17.397652</td>\n",
+ " <td>9.356960</td>\n",
+ " <td>2.12587</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.000000</td>\n",
- " <td>1921.000000</td>\n",
" <td>0.000000</td>\n",
- " <td>NaN</td>\n",
" <td>0.000000</td>\n",
- " <td>5.108000e+03</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
- " <td>NaN</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
- " <td>-60.000000</td>\n",
" <td>0.000000</td>\n",
- " <td>NaN</td>\n",
" <td>0.000000</td>\n",
- " <td>NaN</td>\n",
+ " <td>...</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.00000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
- " <td>0.317000</td>\n",
- " <td>1956.000000</td>\n",
- " <td>0.102000</td>\n",
- " <td>NaN</td>\n",
- " <td>0.415000</td>\n",
- " <td>1.698270e+05</td>\n",
- " <td>0.255000</td>\n",
+ " <td>2.000000</td>\n",
" <td>0.000000</td>\n",
- " <td>NaN</td>\n",
" <td>0.000000</td>\n",
- " <td>2.000000</td>\n",
- " <td>0.098800</td>\n",
- " <td>-14.615000</td>\n",
- " <td>0.000000</td>\n",
- " <td>NaN</td>\n",
- " <td>11.000000</td>\n",
- " <td>NaN</td>\n",
- " <td>0.034900</td>\n",
- " <td>93.421000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>...</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.00000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
- " <td>0.540000</td>\n",
- " <td>1977.000000</td>\n",
- " <td>0.516000</td>\n",
- " <td>NaN</td>\n",
- " <td>0.548000</td>\n",
- " <td>2.074670e+05</td>\n",
- " <td>0.471000</td>\n",
- " <td>0.000000</td>\n",
- " <td>NaN</td>\n",
- " <td>0.000216</td>\n",
- " <td>5.000000</td>\n",
- " <td>0.136000</td>\n",
- " <td>-10.580000</td>\n",
- " <td>1.000000</td>\n",
- " <td>NaN</td>\n",
- " <td>33.000000</td>\n",
- " <td>NaN</td>\n",
- " <td>0.045000</td>\n",
- " <td>114.729000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>75%</th>\n",
- " <td>0.747000</td>\n",
- " <td>1999.000000</td>\n",
- " <td>0.893000</td>\n",
- " <td>NaN</td>\n",
- " <td>0.668000</td>\n",
- " <td>2.624000e+05</td>\n",
- " <td>0.703000</td>\n",
- " <td>0.000000</td>\n",
- " <td>NaN</td>\n",
- " <td>0.102000</td>\n",
- " <td>8.000000</td>\n",
- " <td>0.261000</td>\n",
- " <td>-7.183000</td>\n",
- " <td>1.000000</td>\n",
- " <td>NaN</td>\n",
- " <td>48.000000</td>\n",
- " <td>NaN</td>\n",
- " <td>0.075600</td>\n",
- " <td>135.537000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>max</th>\n",
- " <td>1.000000</td>\n",
- " <td>2020.000000</td>\n",
- " <td>0.996000</td>\n",
- " <td>NaN</td>\n",
- " <td>0.988000</td>\n",
- " <td>5.403500e+06</td>\n",
- " <td>1.000000</td>\n",
- " <td>1.000000</td>\n",
- " <td>NaN</td>\n",
- " <td>1.000000</td>\n",
- " <td>11.000000</td>\n",
- " <td>1.000000</td>\n",
- " <td>3.855000</td>\n",
- " <td>1.000000</td>\n",
- " <td>NaN</td>\n",
- " <td>100.000000</td>\n",
- " <td>NaN</td>\n",
- " <td>0.970000</td>\n",
- " <td>243.507000</td>\n",
+ " <td>4.500000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>...</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.00000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>7.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>...</td>\n",
+ " <td>58.000000</td>\n",
+ " <td>9.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.00000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>9.000000</td>\n",
+ " <td>16.000000</td>\n",
+ " <td>36.000000</td>\n",
+ " <td>226.000000</td>\n",
+ " <td>164.000000</td>\n",
+ " <td>227.000000</td>\n",
+ " <td>230.000000</td>\n",
+ " <td>224.000000</td>\n",
+ " <td>255.000000</td>\n",
+ " <td>254.000000</td>\n",
+ " <td>...</td>\n",
+ " <td>255.000000</td>\n",
+ " <td>255.000000</td>\n",
+ " <td>255.000000</td>\n",
+ " <td>255.000000</td>\n",
+ " <td>255.000000</td>\n",
+ " <td>255.000000</td>\n",
+ " <td>255.000000</td>\n",
+ " <td>255.000000</td>\n",
+ " <td>255.000000</td>\n",
+ " <td>170.00000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
+ "<p>8 rows × 785 columns</p>\n",
"</div>"
],
"text/plain": [
- " valence year acousticness artists \\\n",
- "count 170653.000000 170653.000000 170653.000000 170653 \n",
- "unique NaN NaN NaN 34088 \n",
- "top NaN NaN NaN ['ÐрнеÑÑ‚ ХемингуÑй'] \n",
- "freq NaN NaN NaN 1211 \n",
- "mean 0.528587 1976.787241 0.502115 NaN \n",
- "std 0.263171 25.917853 0.376032 NaN \n",
- "min 0.000000 1921.000000 0.000000 NaN \n",
- "25% 0.317000 1956.000000 0.102000 NaN \n",
- "50% 0.540000 1977.000000 0.516000 NaN \n",
- "75% 0.747000 1999.000000 0.893000 NaN \n",
- "max 1.000000 2020.000000 0.996000 NaN \n",
+ " label pixel1 pixel2 pixel3 pixel4 \\\n",
+ "count 60000.000000 60000.000000 60000.000000 60000.000000 60000.000000 \n",
+ "mean 4.500000 0.000900 0.006150 0.035333 0.101933 \n",
+ "std 2.872305 0.094689 0.271011 1.222324 2.452871 \n",
+ "min 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
+ "25% 2.000000 0.000000 0.000000 0.000000 0.000000 \n",
+ "50% 4.500000 0.000000 0.000000 0.000000 0.000000 \n",
+ "75% 7.000000 0.000000 0.000000 0.000000 0.000000 \n",
+ "max 9.000000 16.000000 36.000000 226.000000 164.000000 \n",
+ "\n",
+ " pixel5 pixel6 pixel7 pixel8 pixel9 \\\n",
+ "count 60000.000000 60000.000000 60000.000000 60000.000000 60000.000000 \n",
+ "mean 0.247967 0.411467 0.805767 2.198283 5.682000 \n",
+ "std 4.306912 5.836188 8.215169 14.093378 23.819481 \n",
+ "min 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
+ "25% 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
+ "50% 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
+ "75% 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
+ "max 227.000000 230.000000 224.000000 255.000000 254.000000 \n",
"\n",
- " danceability duration_ms energy explicit \\\n",
- "count 170653.000000 1.706530e+05 170653.000000 170653.000000 \n",
- "unique NaN NaN NaN NaN \n",
- "top NaN NaN NaN NaN \n",
- "freq NaN NaN NaN NaN \n",
- "mean 0.537396 2.309483e+05 0.482389 0.084575 \n",
- "std 0.176138 1.261184e+05 0.267646 0.278249 \n",
- "min 0.000000 5.108000e+03 0.000000 0.000000 \n",
- "25% 0.415000 1.698270e+05 0.255000 0.000000 \n",
- "50% 0.548000 2.074670e+05 0.471000 0.000000 \n",
- "75% 0.668000 2.624000e+05 0.703000 0.000000 \n",
- "max 0.988000 5.403500e+06 1.000000 1.000000 \n",
+ " ... pixel775 pixel776 pixel777 pixel778 \\\n",
+ "count ... 60000.000000 60000.000000 60000.000000 60000.000000 \n",
+ "mean ... 34.625400 23.300683 16.588267 17.869433 \n",
+ "std ... 57.545242 48.854427 41.979611 43.966032 \n",
+ "min ... 0.000000 0.000000 0.000000 0.000000 \n",
+ "25% ... 0.000000 0.000000 0.000000 0.000000 \n",
+ "50% ... 0.000000 0.000000 0.000000 0.000000 \n",
+ "75% ... 58.000000 9.000000 0.000000 0.000000 \n",
+ "max ... 255.000000 255.000000 255.000000 255.000000 \n",
"\n",
- " id instrumentalness key \\\n",
- "count 170653 170653.000000 170653.000000 \n",
- "unique 170653 NaN NaN \n",
- "top 6Af5jOEKb2bef8XE74ptnw NaN NaN \n",
- "freq 1 NaN NaN \n",
- "mean NaN 0.167010 5.199844 \n",
- "std NaN 0.313475 3.515094 \n",
- "min NaN 0.000000 0.000000 \n",
- "25% NaN 0.000000 2.000000 \n",
- "50% NaN 0.000216 5.000000 \n",
- "75% NaN 0.102000 8.000000 \n",
- "max NaN 1.000000 11.000000 \n",
+ " pixel779 pixel780 pixel781 pixel782 pixel783 \\\n",
+ "count 60000.000000 60000.000000 60000.000000 60000.000000 60000.000000 \n",
+ "mean 22.814817 17.911483 8.520633 2.753300 0.855517 \n",
+ "std 51.830477 45.149388 29.614859 17.397652 9.356960 \n",
+ "min 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
+ "25% 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
+ "50% 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
+ "75% 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
+ "max 255.000000 255.000000 255.000000 255.000000 255.000000 \n",
"\n",
- " liveness loudness mode name \\\n",
- "count 170653.000000 170653.000000 170653.000000 170653 \n",
- "unique NaN NaN NaN 133638 \n",
- "top NaN NaN NaN White Christmas \n",
- "freq NaN NaN NaN 73 \n",
- "mean 0.205839 -11.467990 0.706902 NaN \n",
- "std 0.174805 5.697943 0.455184 NaN \n",
- "min 0.000000 -60.000000 0.000000 NaN \n",
- "25% 0.098800 -14.615000 0.000000 NaN \n",
- "50% 0.136000 -10.580000 1.000000 NaN \n",
- "75% 0.261000 -7.183000 1.000000 NaN \n",
- "max 1.000000 3.855000 1.000000 NaN \n",
+ " pixel784 \n",
+ "count 60000.00000 \n",
+ "mean 0.07025 \n",
+ "std 2.12587 \n",
+ "min 0.00000 \n",
+ "25% 0.00000 \n",
+ "50% 0.00000 \n",
+ "75% 0.00000 \n",
+ "max 170.00000 \n",
"\n",
- " popularity release_date speechiness tempo \n",
- "count 170653.000000 170653 170653.000000 170653.000000 \n",
- "unique NaN 11244 NaN NaN \n",
- "top NaN 1945 NaN NaN \n",
- "freq NaN 1446 NaN NaN \n",
- "mean 31.431794 NaN 0.098393 116.861590 \n",
- "std 21.826615 NaN 0.162740 30.708533 \n",
- "min 0.000000 NaN 0.000000 0.000000 \n",
- "25% 11.000000 NaN 0.034900 93.421000 \n",
- "50% 33.000000 NaN 0.045000 114.729000 \n",
- "75% 48.000000 NaN 0.075600 135.537000 \n",
- "max 100.000000 NaN 0.970000 243.507000 "
+ "[8 rows x 785 columns]"
]
},
- "execution_count": 12,
+ "execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "#descriptive statistics for all columns\n",
- "raw_data.describe(include = 'all')"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 2.3 Datenbereinigung"
+ "df_train.describe()"
]
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 7,
"metadata": {},
"outputs": [
{
@@ -829,362 +754,545 @@
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
- " <th>valence</th>\n",
- " <th>year</th>\n",
- " <th>acousticness</th>\n",
- " <th>artists</th>\n",
- " <th>danceability</th>\n",
- " <th>duration_ms</th>\n",
- " <th>energy</th>\n",
- " <th>explicit</th>\n",
- " <th>id</th>\n",
- " <th>instrumentalness</th>\n",
- " <th>key</th>\n",
- " <th>liveness</th>\n",
- " <th>loudness</th>\n",
- " <th>mode</th>\n",
- " <th>name</th>\n",
- " <th>popularity</th>\n",
- " <th>release_date</th>\n",
- " <th>speechiness</th>\n",
- " <th>tempo</th>\n",
+ " <th>label</th>\n",
+ " <th>pixel1</th>\n",
+ " <th>pixel2</th>\n",
+ " <th>pixel3</th>\n",
+ " <th>pixel4</th>\n",
+ " <th>pixel5</th>\n",
+ " <th>pixel6</th>\n",
+ " <th>pixel7</th>\n",
+ " <th>pixel8</th>\n",
+ " <th>pixel9</th>\n",
+ " <th>...</th>\n",
+ " <th>pixel775</th>\n",
+ " <th>pixel776</th>\n",
+ " <th>pixel777</th>\n",
+ " <th>pixel778</th>\n",
+ " <th>pixel779</th>\n",
+ " <th>pixel780</th>\n",
+ " <th>pixel781</th>\n",
+ " <th>pixel782</th>\n",
+ " <th>pixel783</th>\n",
+ " <th>pixel784</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>10000.000000</td>\n",
+ " <td>10000.000000</td>\n",
+ " <td>10000.000000</td>\n",
+ " <td>10000.000000</td>\n",
+ " <td>10000.000000</td>\n",
+ " <td>10000.000000</td>\n",
+ " <td>10000.000000</td>\n",
+ " <td>10000.000000</td>\n",
+ " <td>10000.000000</td>\n",
+ " <td>10000.000000</td>\n",
+ " <td>...</td>\n",
+ " <td>10000.000000</td>\n",
+ " <td>10000.000000</td>\n",
+ " <td>10000.000000</td>\n",
+ " <td>10000.000000</td>\n",
+ " <td>10000.000000</td>\n",
+ " <td>10000.000000</td>\n",
+ " <td>10000.000000</td>\n",
+ " <td>10000.000000</td>\n",
+ " <td>10000.000000</td>\n",
+ " <td>10000.00000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>4.500000</td>\n",
+ " <td>0.000400</td>\n",
+ " <td>0.010300</td>\n",
+ " <td>0.052100</td>\n",
+ " <td>0.077000</td>\n",
+ " <td>0.208600</td>\n",
+ " <td>0.349200</td>\n",
+ " <td>0.826700</td>\n",
+ " <td>2.321200</td>\n",
+ " <td>5.457800</td>\n",
+ " <td>...</td>\n",
+ " <td>34.320800</td>\n",
+ " <td>23.071900</td>\n",
+ " <td>16.432000</td>\n",
+ " <td>17.870600</td>\n",
+ " <td>22.860000</td>\n",
+ " <td>17.790200</td>\n",
+ " <td>8.353500</td>\n",
+ " <td>2.541600</td>\n",
+ " <td>0.629500</td>\n",
+ " <td>0.06560</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>2.872425</td>\n",
+ " <td>0.024493</td>\n",
+ " <td>0.525187</td>\n",
+ " <td>2.494315</td>\n",
+ " <td>2.208882</td>\n",
+ " <td>4.669183</td>\n",
+ " <td>5.657849</td>\n",
+ " <td>8.591731</td>\n",
+ " <td>15.031508</td>\n",
+ " <td>23.359019</td>\n",
+ " <td>...</td>\n",
+ " <td>57.888679</td>\n",
+ " <td>49.049749</td>\n",
+ " <td>42.159665</td>\n",
+ " <td>44.140552</td>\n",
+ " <td>51.706601</td>\n",
+ " <td>45.128107</td>\n",
+ " <td>28.765769</td>\n",
+ " <td>16.417363</td>\n",
+ " <td>7.462533</td>\n",
+ " <td>1.93403</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>...</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.00000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>2.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>...</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.00000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>4.500000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>...</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.00000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>7.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>...</td>\n",
+ " <td>55.000000</td>\n",
+ " <td>6.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.00000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>9.000000</td>\n",
+ " <td>2.000000</td>\n",
+ " <td>45.000000</td>\n",
+ " <td>218.000000</td>\n",
+ " <td>185.000000</td>\n",
+ " <td>227.000000</td>\n",
+ " <td>223.000000</td>\n",
+ " <td>247.000000</td>\n",
+ " <td>218.000000</td>\n",
+ " <td>244.000000</td>\n",
+ " <td>...</td>\n",
+ " <td>254.000000</td>\n",
+ " <td>252.000000</td>\n",
+ " <td>255.000000</td>\n",
+ " <td>255.000000</td>\n",
+ " <td>255.000000</td>\n",
+ " <td>255.000000</td>\n",
+ " <td>240.000000</td>\n",
+ " <td>225.000000</td>\n",
+ " <td>205.000000</td>\n",
+ " <td>107.00000</td>\n",
+ " </tr>\n",
" </tbody>\n",
"</table>\n",
+ "<p>8 rows × 785 columns</p>\n",
"</div>"
],
"text/plain": [
- "Empty DataFrame\n",
- "Columns: [valence, year, acousticness, artists, danceability, duration_ms, energy, explicit, id, instrumentalness, key, liveness, loudness, mode, name, popularity, release_date, speechiness, tempo]\n",
- "Index: []"
+ " label pixel1 pixel2 pixel3 pixel4 \\\n",
+ "count 10000.000000 10000.000000 10000.000000 10000.000000 10000.000000 \n",
+ "mean 4.500000 0.000400 0.010300 0.052100 0.077000 \n",
+ "std 2.872425 0.024493 0.525187 2.494315 2.208882 \n",
+ "min 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
+ "25% 2.000000 0.000000 0.000000 0.000000 0.000000 \n",
+ "50% 4.500000 0.000000 0.000000 0.000000 0.000000 \n",
+ "75% 7.000000 0.000000 0.000000 0.000000 0.000000 \n",
+ "max 9.000000 2.000000 45.000000 218.000000 185.000000 \n",
+ "\n",
+ " pixel5 pixel6 pixel7 pixel8 pixel9 \\\n",
+ "count 10000.000000 10000.000000 10000.000000 10000.000000 10000.000000 \n",
+ "mean 0.208600 0.349200 0.826700 2.321200 5.457800 \n",
+ "std 4.669183 5.657849 8.591731 15.031508 23.359019 \n",
+ "min 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
+ "25% 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
+ "50% 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
+ "75% 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
+ "max 227.000000 223.000000 247.000000 218.000000 244.000000 \n",
+ "\n",
+ " ... pixel775 pixel776 pixel777 pixel778 \\\n",
+ "count ... 10000.000000 10000.000000 10000.000000 10000.000000 \n",
+ "mean ... 34.320800 23.071900 16.432000 17.870600 \n",
+ "std ... 57.888679 49.049749 42.159665 44.140552 \n",
+ "min ... 0.000000 0.000000 0.000000 0.000000 \n",
+ "25% ... 0.000000 0.000000 0.000000 0.000000 \n",
+ "50% ... 0.000000 0.000000 0.000000 0.000000 \n",
+ "75% ... 55.000000 6.000000 0.000000 0.000000 \n",
+ "max ... 254.000000 252.000000 255.000000 255.000000 \n",
+ "\n",
+ " pixel779 pixel780 pixel781 pixel782 pixel783 \\\n",
+ "count 10000.000000 10000.000000 10000.000000 10000.000000 10000.000000 \n",
+ "mean 22.860000 17.790200 8.353500 2.541600 0.629500 \n",
+ "std 51.706601 45.128107 28.765769 16.417363 7.462533 \n",
+ "min 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
+ "25% 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
+ "50% 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
+ "75% 1.000000 0.000000 0.000000 0.000000 0.000000 \n",
+ "max 255.000000 255.000000 240.000000 225.000000 205.000000 \n",
+ "\n",
+ " pixel784 \n",
+ "count 10000.00000 \n",
+ "mean 0.06560 \n",
+ "std 1.93403 \n",
+ "min 0.00000 \n",
+ "25% 0.00000 \n",
+ "50% 0.00000 \n",
+ "75% 0.00000 \n",
+ "max 107.00000 \n",
+ "\n",
+ "[8 rows x 785 columns]"
]
},
- "execution_count": 13,
+ "execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "# check for duplicate rows\n",
- "raw_data[raw_data.duplicated(keep = False)]"
+ "df_test.describe()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Sowohl die Testdaten als auch die Trainingsdaten scheinen gültig und unbeschädigt zu sein."
]
},
{
- "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Auf fehlende Werte prüfen"
+ "Für Menschen lesbare Namen für 10 Kategorien definieren."
]
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 8,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "valence 0\n",
- "year 0\n",
- "acousticness 0\n",
- "artists 0\n",
- "danceability 0\n",
- "duration_ms 0\n",
- "energy 0\n",
- "explicit 0\n",
- "id 0\n",
- "instrumentalness 0\n",
- "key 0\n",
- "liveness 0\n",
- "loudness 0\n",
- "mode 0\n",
- "name 0\n",
- "popularity 0\n",
- "release_date 0\n",
- "speechiness 0\n",
- "tempo 0\n",
- "dtype: int64"
- ]
- },
- "execution_count": 14,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
- "raw_data.isnull().sum()"
+ "class_names = ['Top','Trouser','Pullover','Dress','Coat',\n",
+ " 'Sandal','Shirt','Sneaker','Bag','Ankle boot']"
]
},
{
- "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Auswahl der Prädiktoren"
+ "Teile den Datensatz und überprüfe die Verteilung jeder Klasse (das Teilen kann auch später erfolgen)."
]
},
{
"cell_type": "code",
- "execution_count": 15,
- "metadata": {
- "scrolled": true
- },
+ "execution_count": 9,
+ "metadata": {},
"outputs": [
{
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 1296x1296 with 2 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "54000 train examples\n",
+ "6000 validation examples\n",
+ "10000 test examples\n"
+ ]
}
],
"source": [
- "#predictive modeling\n",
- "#plot a correlation matrix to find out which variables are correlated to each other\n",
- "f,ax=plt.subplots(figsize = (18,18))\n",
- "sns.heatmap(raw_data.corr(),annot= True,linewidths=0.2, fmt = \".1f\",ax=ax)\n",
- "plt.xticks(rotation=90)\n",
- "plt.yticks(rotation=0)\n",
- "plt.title('Correlation Map')\n",
- "plt.show()"
+ "df_train, df_val = train_test_split(df_train, test_size=0.1, random_state=365)\n",
+ "print(f\"{len(df_train)} train examples\")\n",
+ "print(f\"{len(df_val)} validation examples\")\n",
+ "print(f\"{len(df_test)} test examples\")"
]
},
{
- "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Features entfernen"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {},
- "outputs": [],
- "source": [
- "raw_data= raw_data.drop(['name', 'release_date'], axis=1)"
+ "Zeige die Verteilung für jedes Datenset"
]
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 10,
"metadata": {},
"outputs": [
{
- "ename": "KeyError",
- "evalue": "\"['id'] not found in axis\"",
- "output_type": "error",
- "traceback": [
- "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)",
- "\u001b[1;32m<ipython-input-18-5840d43c2e41>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mraw_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mraw_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'id'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
- "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36mdrop\u001b[1;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[0;32m 4161\u001b[0m \u001b[0mweight\u001b[0m \u001b[1;36m1.0\u001b[0m \u001b[1;36m0.8\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4162\u001b[0m \"\"\"\n\u001b[1;32m-> 4163\u001b[1;33m return super().drop(\n\u001b[0m\u001b[0;32m 4164\u001b[0m \u001b[0mlabels\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4165\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
- "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36mdrop\u001b[1;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[0;32m 3885\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[1;32min\u001b[0m \u001b[0maxes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3886\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mlabels\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3887\u001b[1;33m \u001b[0mobj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_drop_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3888\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3889\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0minplace\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
- "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m_drop_axis\u001b[1;34m(self, labels, axis, level, errors)\u001b[0m\n\u001b[0;32m 3919\u001b[0m \u001b[0mnew_axis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3920\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3921\u001b[1;33m \u001b[0mnew_axis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3922\u001b[0m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0maxis_name\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mnew_axis\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3923\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
- "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mdrop\u001b[1;34m(self, labels, errors)\u001b[0m\n\u001b[0;32m 5280\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0many\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5281\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0merrors\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;34m\"ignore\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5282\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"{labels[mask]} not found in axis\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 5283\u001b[0m \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m~\u001b[0m\u001b[0mmask\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5284\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdelete\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
- "\u001b[1;31mKeyError\u001b[0m: \"['id'] not found in axis\""
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "TRAIN DISTRIBUTION\n",
+ "\n",
+ "Sandal : 5429 or 10.053703703703704%\n",
+ "Coat : 5421 or 10.03888888888889%\n",
+ "Pullover : 5407 or 10.012962962962963%\n",
+ "Dress : 5405 or 10.00925925925926%\n",
+ "Ankle boot : 5404 or 10.007407407407408%\n",
+ "Shirt : 5397 or 9.994444444444445%\n",
+ "Top : 5396 or 9.992592592592594%\n",
+ "Sneaker : 5395 or 9.99074074074074%\n",
+ "Trouser : 5384 or 9.97037037037037%\n",
+ "Bag : 5362 or 9.92962962962963%\n",
+ "\n",
+ "VALIDATION DISTRIBUTION\n",
+ "\n",
+ "Bag : 638 or 10.633333333333333%\n",
+ "Trouser : 616 or 10.266666666666667%\n",
+ "Sneaker : 605 or 10.083333333333332%\n",
+ "Top : 604 or 10.066666666666666%\n",
+ "Shirt : 603 or 10.05%\n",
+ "Ankle boot : 596 or 9.933333333333334%\n",
+ "Dress : 595 or 9.916666666666666%\n",
+ "Pullover : 593 or 9.883333333333333%\n",
+ "Coat : 579 or 9.65%\n",
+ "Sandal : 571 or 9.516666666666666%\n",
+ "\n",
+ "TEST DISTRIBUTION\n",
+ "\n",
+ "Top : 1000 or 10.0%\n",
+ "Trouser : 1000 or 10.0%\n",
+ "Pullover : 1000 or 10.0%\n",
+ "Dress : 1000 or 10.0%\n",
+ "Bag : 1000 or 10.0%\n",
+ "Shirt : 1000 or 10.0%\n",
+ "Sandal : 1000 or 10.0%\n",
+ "Coat : 1000 or 10.0%\n",
+ "Sneaker : 1000 or 10.0%\n",
+ "Ankle boot : 1000 or 10.0%\n"
]
}
],
"source": [
- "raw_data = raw_data.drop(['id'], axis=1)"
+ "def get_classes_distribution(data):\n",
+ " # Get the count for each label\n",
+ " label_counts = data[\"label\"].value_counts()\n",
+ "\n",
+ " # Get total number of samples\n",
+ " total_samples = len(data)\n",
+ "\n",
+ "\n",
+ " # Count the number of items in each class\n",
+ " for i in range(len(label_counts)):\n",
+ " label = class_names[label_counts.index[i]]\n",
+ " count = label_counts.values[i]\n",
+ " percent = (count / total_samples) * 100\n",
+ " print(\"{:<20s}: {} or {}%\".format(label, count, percent))\n",
+ "\n",
+ "print(\"\\nTRAIN DISTRIBUTION\\n\")\n",
+ "get_classes_distribution(df_train)\n",
+ "print(\"\\nVALIDATION DISTRIBUTION\\n\")\n",
+ "get_classes_distribution(df_val)\n",
+ "print(\"\\nTEST DISTRIBUTION\\n\")\n",
+ "get_classes_distribution(df_test)"
]
},
{
- "cell_type": "code",
- "execution_count": 19,
+ "cell_type": "markdown",
"metadata": {},
- "outputs": [],
"source": [
- "raw_data = raw_data.drop(['artists', 'energy'], axis=1)"
+ "Das ist bereits hilfreich, wir können sehen, dass die Verteilung ziemlich gleichmäßig ist.\n",
+ "Drucke die Daten als Tortendiagramm aus, um es noch ansprechender zu gestalten."
]
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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fvp01a9aQk5PDggULnM758MMP0el0/Pnnn7z99ttnHbsQQrgjKeEJ4aa2bt3Kp59+Slxc3Fm39fPPP7N3716SkpKIiYkB4OOPP6Zfv35s27aNESNGcOmll/Laa6/x6KOPAnD06FF27NjBRx99BMCbb77J0KFDeeKJJxztvvfee8TExHD06FF69uwJQPfu3XnmmWfOOmYhhHBn0gMlhBv5/vvv8ff3x9fXlzFjxjBhwgReffXVs2730KFDxMTEOJIngL59+xIcHMyhQ4cAWLhwISkpKWzevBmATz75hMGDB9O3b18AduzYwYYNG/D393f89O7dG4CEhARHu8OHDz/reIUQwt1JD5QQbmTy5Mm8+eabeHt7ExUVhbe3NwBeXl6OcUh/a0ppT1EUNBrNKbd36NCByZMn8+mnnzJ69GhWrFjB9ddf7zjWbrcze/Zsnn766TrtdOjQwfH/RqOx0XEJIURrJQmUEG7EaDTSvXv3OtvbtWvH/v37nbbt3r3bkWCdTt++fUlNTSUtLc3RC3Xw4EFKSkro06eP47hLL72Ue+65h0WLFpGQkMDChQsd+4YOHcqXX35JbGwsOp28dQgh2jYp4QnRCkyZMoXt27fz0UcfcezYMR588ME6CdWpTJ06lYEDB3LppZeyc+dOtm7dypIlS5g4caJTye2iiy6itLSUG2+8kcmTJ9OxY0fHvmXLllFYWMiiRYvYunUriYmJrF27lquuugqbzebS5yuEEO5OEighWoHp06fzwAMPcPfddzNixAjKyspYsmRJo8/XaDR8/fXXhISEMGHCBKZOnUrXrl1ZuXKl03GBgYHMnj2bPXv2cOmllzrti4qK4s8//8RmszF9+nT69+/PbbfdRlBQEF5e8lYihGhbNMrJAyuEEEIIIcQpyddGIYQQQogmkgRKCCGEEKKJJIESQgghhGgiSaCEEEIIIZpIEighhBBCiCaSBEoIIYQQookkgRJCCCGEaCJJoIQQQgghmkgSKCGEEEKIJpIESgghhBCiiSSBEkIIIYRoIkmghBBCCCGaSBIoIYQQQogmkgRKCCGEEKKJJIESQgghhGgindoBCCE8h6IoWOwWrHar478KCgBeGi+88EKj0dT8v8YLDRq0Xlp8tD4qRy6EEE0jCZQQoo4SUwmF1YUUVhdSVF1EYXUhBdUFFFYVUmSqefz3/5tsJqx2K1a7FZtiO6PreXt5E+QTRLBPMIH6QIJ9ggn2DSZIH0SQT5BjX+3/D/YJRq/Vu/iZCyFE42gURVHUDkII0TIURSGrIouE4gQSSxLJqcxxJEOOZMlUiNVuVTvURjHoDATqAwn1DSUmIIbYoFhiA2PpEtSF2MBY/PX+aocohPBQkkAJ4YEURSG9PJ3E4kQSShJqEqbiRBJLEqm0VqodXosJN4QTGxhL58DOjqQqNiiWaP9otF5atcMTQrRikkAJ0YrZFTvpZenEF8eTWJJIQnFNspRcmkyVtUrt8NyWzktX02N1PKHqEtiF7sHd6R3WG28vb7XDE0K0ApJACdGK5FTksD1nO9uyt3Gg4ABJJUmYbCa1w/IYBp2B/uH9GdxuMEMjhjK43WApAwoh6iUJlBBuLKs8y5Ewbc/ZTlpZmtohtSleGi96BPdgSPshDI0YytD2Q4kwRqgdlhDCDUgCJYQbSS9LdyRMO3J2kFGeoXZI4iRRxigGtx/M0PZDGRIxhB7BPdBoNGqHJYRoYZJACaGi1NJUpx6m7IpstUMSTRSoD2RQu0EMjRjKuKhx9Anro3ZIQogWIAmUEC2oylrFnxl/sj51PVuytpBblat2SMLFIo2RTIyeyOSYyYyMHIm3VgalC+GJJIESopmVmErYmL6Rn1N+ZlPmJqpt1WqHJFqI0dvI2KixTIqZxISOEwj2DVY7JCGEi0gCJUQzKCjPZV36en5O/Zkd2TuwKq1jYkrRfAYGdeOTYiv0mwt95oB/O7VDEkKcBUmghHARa1ERZT+tpXT1aoq9qrlsyiG1QxJu5PaAfly998eaBxotdB4ryZQQrZgkUEKcBVt5BeW//EzJ6tVU/LUJrMd7mnQ6bl3uT7a2XN0Ahdv4vkxH5/zEujtqJ1P9LgK/0BaPTQjRdJJACdFEis1G+YYNlHz3PeUbN6JU1z+madOVw3gxck8LRyfcUXf/GL7a9+fpD9T5Qt+5MOJqiBnZ7HEJIc6cTu0AhGgtrIWFFH/+OUUrP8ealXXa44ceMEFkCwQm3N4UbXDjDrRWw97Pan4iBsDwpTDwEvAJaM7whBBnQHqghDiNqj17KPzkE8rW/IRiNjf+RJ2Om+80kutV0XzBiVbh82p/+mQdPLOT9f4wYH5Nr1TkANcGJoQ4Y5JACVEPu9lM6eofKPr0U6r37Tvjdv66chgvSRmvTevoF8GaA9tc01j0CBh+NfS7ELx9XdOmEOKMSAlPiFosmZkUrfiM4i+/xFZYeNbtDZMyXps3Re/CtfPSt9X8/HQfDFoMw6+C8O6ua98NnG5ZnCuuuIIPPvigZYIR4hSkB0oIoGLzZoo++YSy9RvAZnNdw1LGa/M+sIUzLHVnM7WugS7ja3qles8Cbev/TpydfWI5o5UrV/Kvf/2LI0eOOLYZDAaCgoIcjy0WC97e7jfbu9lsRq/Xqx2GaEZeagcgTm3p0qVoNBo0Gg3e3t5EREQwbdo03nvvPex2u9rhtWr2igoKP/2UhFmzSF16JWXrfnZt8gRgtbI417N6CETjhfqEMCRtdzNeQYGk3+CLK+DFfrDpdbBUNeP1ml9kZKTjJygoCI1G43hcXV1NcHAwn3/+OZMmTcLX15f//ve/2O12HnnkEaKjo/Hx8WHw4MGsWbPG0eavv/6KRqOhuLjYsW337t1oNBqSk5MBSElJYfbs2YSEhGA0GunXrx8//PCD4/iDBw8yc+ZM/P39iYiI4PLLLyc/P9+xf9KkSdx8880sX76c8PBwpk2b1uy/K6EuSaBagRkzZpCVlUVycjI//vgjkydP5rbbbmPWrFlYrfXPcG2xWFo4ytbDVlxM7vPPc2ziJHIeeRRzfEKzXm/YwSYMPBceZbJfNF5KC33RKc+Gn/4PXh4Em98Ei+cuGXTPPfdw6623cujQIaZPn87LL7/M888/z3PPPcfevXuZPn06F1xwAceOHWt0m8uWLcNkMvHbb7+xb98+nn76afz9/QHIyspi4sSJDB48mO3bt7NmzRpycnJYsGCBUxsffvghOp2OP//8k7ffftulz1m4H0mgWgEfHx8iIyPp2LEjQ4cO5f/+7//45ptv+PHHHx1jATQaDW+99RZz5szBaDTy2GOPAfDdd98xbNgwfH196dq1Kw8//LBT0vXQQw/RqVMnfHx8iIqK4tZbb3Xse+ONN+jRowe+vr5EREQwb968Fn3ermYrryDvtdeJnzqNgv+8g728ZSa59Nl5mPY2/xa5lnAvU4sLWv6i5Tmw5t7jidRbHplI3X777Vx00UV06dKFqKgonnvuOe655x4WLlxIr169ePrppxk8eDAvvfRSo9tMTU1l3LhxDBgwgK5duzJr1iwmTJgAwJtvvsnQoUN54okn6N27N0OGDOG9995jw4YNHD161NFG9+7deeaZZ+jVqxe9e/d29dMWbqb1F8zbqClTpjBo0CBWrVrFNddcA8CDDz7Ik08+yYsvvohWq+Wnn37isssu45VXXmH8+PEkJCRw3XXXOY793//+x4svvshnn31Gv379yM7OZs+emjvGtm/fzq233srHH3/M2LFjKSws5Pfff1ft+Z4Ne1UVRZ98QsE772Kr1YXfYiwWFuV34+UIuRuvLQnw9mdU/A71AijPhjX3wJ8vwTnLYdgVoPNRLx4XGj58uOP/S0tLyczMZNy4cU7HjBs3zvF+1hi33norN954I2vXrmXq1KlcfPHFDBw4EIAdO3awYcMGR49UbQkJCfTs2bNOXMLzSQLVivXu3Zu9e/c6Hi9evJirrrrK8fjyyy/n3nvv5YorrgCga9euPProo9x99908+OCDpKamEhkZydSpU/H29qZTp06MHFkz+3FqaipGo5FZs2YREBBA586dGTJkSMs+wbOkmM0Uff4F+W+/hS0v//QnNKPhB83gwpuxhPsb7x+Lt/0M535ypbIs+PGu44nUHTD0CtC17sHNRqOxzraT795TFMWxzcvLy7HtbycPc7jmmmuYPn06q1evZu3atTz55JM8//zz3HLLLdjtdmbPns3TTz9d57odOnQ4ZVzCc0kJrxWr/QYBdb/97Nixg0ceeQR/f3/Hz7XXXktWVhaVlZXMnz+fqqoqunbtyrXXXstXX33lKO9NmzaNzp0707VrVy6//HI++eQTKisrW/T5nSnFaqX4f/8jfsYMch57TPXkCcBnh5Tx2pqpLVQibrTSDPjhH/DKENj2Dlg9Y2xeYGAgUVFR/PHHH07b//rrL/r06QNAu3Y1izVn1VpBYPfu3XXaiomJ4YYbbmDVqlXceeed/Oc//wFg6NChHDhwgNjYWLp37+70I0lT2yUJVCt26NAhunTp4nh88h+y3W7n4YcfZvfu3Y6fffv2cezYMXx9fYmJieHIkSO8/vrrGAwGbrrpJiZMmIDFYiEgIICdO3eyYsUKOnTowL/+9S8GDRrkdBeLu1Hsdkq++57E82eR9c8HsGaefrmVFmOxsDC/m9pRiBbiq/VhXLKK5btTKU2H1XfCq0Nh+3tga/03nNx11108/fTTrFy5kiNHjnDvvfeye/dubrvtNqBmbFJMTAwPPfQQR48eZfXq1Tz//PNObdx+++389NNPJCUlsXPnTtavX+9IwJYtW0ZhYSGLFi1i69atJCYmsnbtWq666ipsrr5zV7QaUsJrpdavX8++ffu44447Gjxm6NChHDlyhO7dG76N3mAwcMEFF3DBBRewbNkyevfuzb59+xg6dCg6nY6pU6cydepUHnzwQYKDg1m/fj0XXXRRczyls1K6bh35r7yKqQl33bS0EVLGazPGBHbDz+y+r0UAStLg+zvg9xdh/HIYugS8tGpHdUZuvfVWSktLufPOO8nNzaVv3758++239OjRAwBvb29WrFjBjTfeyKBBgxgxYgSPPfYY8+fPd7Rhs9lYtmwZ6enpBAYGMmPGDF588UUAoqKi+PPPP7nnnnuYPn06JpOJzp07M2PGDEd5ULQ9MpGmm1u6dCk5OTm8//772Gw2cnJyWLNmDU8++SSTJk3i66+/RqvVotFo+Oqrr5g7d67j3J9++olZs2Zx//33M3/+fLy8vNi7dy/79u3jscce44MPPsBmszFq1Cj8/Px47733eOGFF0hLS2PTpk0kJiYyYcIEQkJC+OGHH7j55pvZu3cv/fr1U+8XcpLy338n7+VXqN6/X+1QTkuj13PjHQbyZVJNj/eYbw/mHPpF7TCaJqI/nP88dBqtdiRCtAqSOrcCa9asoUOHDsTGxjJjxgw2bNjAK6+8wjfffINW2/A3xunTp/P999+zbt06RowYwejRo3nhhRfo3LkzAMHBwfznP/9h3LhxDBw4kF9++YXvvvuOsLAwgoODWbVqFVOmTKFPnz689dZbrFixwm2SJ3NqKqnXXEvatde1iuQJaga1L8qXSTU9nU6jY5K7lu9OJWc/vDcDvr4JKtQfNyiEu5MeKNGqKBYLBe++S/6bb6GYTGqH02TVYwayZJIb3Jklms2ooJ68s/tntcM4O77BEPcADLsKpEQlRL3kL0O0GpXbt5N44YXkvfRyq0yeAAw7DhNul7t2PNlUqwe8rVYX1ww0f2cKZLTC3jQhWoAH/KULT2crLibz/vtJuXxJsy+70twUs1nuxvNgGjRMSdundhiuk7kL3pkK390OVUVqRyOEW5EESri14q+/JmHm+ZR8uQo8pNo88mD96xeK1m9AYFfal7jR9BmuoNhhx/vw6nDY9V+P+TsU4mxJAiXckikxiZQrlpJ1733YCgvVDselDDuPEGb3UzsM0QziFF+1Q2g+lfnwzTJ4bzpke1AvmxBnSBIo4VbsZjN5r7xK0ty5VG7ZonY4zUIxmeRuPA81NfOw2iE0v7Qt8PZE+PEeqC5VOxohVCMJlHAbFZs3k3TBHPLfeAPF7BnLTDREyniep7t/DJ3yk9QOo2UoNtjyFrw2AvavUjsaIVQhCZRQnbWwkMx77iF16ZWYk5PVDqdFSBnP80zVBqsdQssrz4b/XQn/uxqqitWORogWJQmUUFXZhg0kzppNyTffqh1Ki1JMJhZKGc+jTM1uI71P9dn/P3hzHCT9pnYkQrQYSaCEKuzV1WQ/8gjpN97kcYPEG2vkISnjeYpov0h6ZbfxCVJL0+HDC+Cn+8HaOudpE6IpJIESLa76yFGS58+n6NMVaoeiKr8dUsbzFHH69mqH4CYU2PQa/GcK5LTxhFJ4PEmgRIsq/OgjkufPx3QsXu1QVKeYTFxSIGU8TxCXn6F2CO4lZz/8exJsekPtSIRoNpJAiRZhLSgg9frryXniSY+/w64pRh2yqR2COEvhPqEMTtutdhjux2aCn+6DTy+ByrZZpheeTRIo0ewqtmwlce5cKjbKANOT+W0/TIjdoHYY4ixM9uuIBpmdu0FH18Bb50DKX2pHIoRLSQIlmo2iKOS/9RapV12FLS9f7XDckmIysaigh9phiLMwtbhA7RDcX2kGfDALNj4Ddrva0QjhEpJAiWZhLSoi7brryXvpZbBJmepUpIzXegV4+zMieYfaYbQOig02PA4fz4WyHLWjEeKsSQIlXK5y5y6SLryIit9/VzuUVsFvxxGC7B68hpoHm+gfi7fdonYYrUvSRnhrHCRuVDsSIc6KJFDCpQrefY+UJUuwZmerHUqroVRXs7igp9phiDMQV1amdgitU0Ue/Pci2PGB2pEIccYkgRIuYTebyVh+J7nPPgtWmSCyqUYdljJea+Or9WFcipTvzpjdCt/dVjPxpoyLEq2QJFDirFmLiki98ipKf/hB7VBaLeN2KeO1NmMDu2EwV6odRuu36TVYeSmYK9SORIgmkQRKnBVzSgopCxdRtUO+iZ8NpbqaRYVyN15rMrVK5jNzmSM/wHvToUQmJBWthyRQ4oxV7txF8sJFmFNS1A7FI4w+JHMJtRY6Lx0TkneqHYZnyd4H78RB5i61IxGiUSSBEmekdM0aUq+8EltRkdqheAzjjsNSxmslRgR2I6iqWO0wPE9ZFrw/Ew5+q3YkQpyWJFCiyQreeYeMO5ajmGTFdVdSqqSM11pMtWjUDsFzWSrh8yXw+wtqRyLEKUkCJRpNsdnIevAhcp97HhQpNzUHKeO5Py+NF1NS9qgdhodT4JeH4etlYJN5toR7kgRKNIq9ooK0G2+keOVKtUPxaMYdhwlSpIznzgYGdCFcZtJuGbv/Cx/NlcWIhVuSBEqcliUnh+TLLqfiN5lZvLkpVdUslLXx3Fqc4qN2CG1Lyh81g8vz49WORAgnkkCJU6o+coTkSxZiOnRI7VDajNGHpYznzuIy5G+hxRUm1iRRKZvUjkQIB0mgRIPKf/+DlMWXyrIsLcx/u5Tx3FVP/07EFMi0HaqoLob/XgzJf6odiRCAJFCiAaU//kjajTdir5DZgVuaUlXNJQXd1Q5D1GOqNkjtENo2SwV8Mh+SZDiBUJ8kUKKO0jU/kXHX3bKmnYrGHFE7AlGfKVkJaocgLBXw6QJI+k3tSEQbJwmUcFK6bh0Z//iHJE8q8992hAC7DFZ2JzF+kfTKOax2GAJq5or6ZAEk/qp2JKINkwRKOJStX0/G8jsleXIDSlUVC4t6qh2GqCVO307tEERt1ir4dCEkbFA7EtFGSQIlACj79VcybrsdLDJpnbsYK50dbiUuXxa6dTvWKlixEOJ/UTsS0QZJAiUo/+03Mm69DUWSJ7fiv+2wlPHcRDvfUAalyezjbslaDZ8thvif1Y5EtDGSQLVx5X/8Sfott6KYzWqHIk6iVFVxSZFMqukOpvh2RIPMz+W2rNWwYjEcW6d2JKINkQSqDavYtIn0m2+WRYHd2NgjsmitO4grzlM7BHE6NhN8dikcXat2JKKNkASqjarYvIW0G29Cqa5WOxRxCgHbjuCv6NUOo00L1AcwImWn2mGIxrCZYOWlcGSN2pGINkASqDaocts20m6S5Kk1UCoruUTuxlPVJGMsOrvcmdpq2Mzw+eVw5Ee1IxEeThKoNqZyxw7Srr8BpbJS7VBEI42Tu/FUNaWsRO0QRFPZzPD5Ejj6k9qRCA8mCVQbUrlrF2nXXY9dkqdWJWDbUSnjqcSg9WVc8g61wxBnwmaGL66EzN1qRyI8lCRQbUT10aOkXXudrG3XCkkZTz3jArvia6lSOwxxpiwVNfNElcgcXsL1JIFqA6wFBaTfeBP28nK1QxFnaNxhuRtPDXGVMr1Hq1eWVbN2nqlM7UiEh9EoiiKTm3gwu9lM6pIrqNq9W+1QxFnQ+Plx5a1Qrmn+D/SKIxXk/5BPVUoV1mIrnW7pROCwQMd+RVHI/TqXoo1F2CpsGLoaiFoShW9H31O2W7KthNyvcjHnmtG31xNxcYRTu8V/FZP9v2wUk0LI+BAiF0Y69pnzzCQ/l0y3h7qhNWhd/6TrofPS8VtGAQHVMgbKI3SfCos/B6+Wef0Izyc9UB4u6//ul+TJAyiVlSwobJkynt1kx7eTLx0u61Dv/vwf8in4qYAOl3Wg24Pd8A7yJvnZZGxVtgbbrIyvJO3NNILHBtP9ke4Ejw0m9Y1UKhNqxuNZy6xkvJ9Bh0s60PnOzhT9WUTZ7hM9BpkfZRIxP6LFkieAUYHdJXnyJPE/ww//UDsK4UEkgfJgeW+8Qen336sdhnCRcUdbpowXMDCAiIsjCBoeVGefoigUrC2g3ex2BA0Pwjfal47XdsRuslOyueFkI39tPv79/Gk3qx0+UT60m9UO/z7+FKwtAGp6mLQGLUGjgvDr6oexj5HqzJppNoo3FaPRaeqNpzlNMUvnvMfZ/h789araUQgPIQmUhypds4b8V19TOwzhQoFucDeeJc+CtcSKf39/xzYvby+MvY1Uxjd8d2dVfJXTOQD+A/wd5/hE+GA322vKhuVWqpKq8I3xxVpuJfer3AZ7w5qLl8aLKSmy9p1HWvcvOPit2lEIDyAJlAeq2rePzHvvAxne5lGUigrmq7w2nrWkZkJJXaDOabsuUOfY19B5pzpHa9QSfW006f9JJ/GRRILHBhMwIIDsldmETg3Fkm8h/l/xHLv/GCXbmr+sNiigC+Hluc1+HaECxQ6rroN0mZ5CnB3d6Q8RrYklO1tmGfdg5xz14v3RakcBnFxNbEyufppzAocFOg0qLz9UjindRNRlURy95ygxN8SgC9KR8EgCxl7GOgmZK8Uppx4QL1o5a1XN9AbX/gLBndSORrRS0gPlQeyVlaTdeBO2vHy1QxHNJHDbUfzs3qpdXxdUk7Sc3NtkLbM69jV0XlPOsVvsZH2cRdQVUZhzzSg2BWNvIz4dfPCJ9HEMPm8ucekHmrV94QYqcuGT+SA3CogzJAmUh1DsdjLuuhvToUNqhyKakVJewYIS9SbV9G7njS5IR/mBE3OK2a12Kg5X4Nfdr8HzDN0NTucAlO8vb/CcvG/z8B/gjyHWgGJXwH5in2J1fuxqvQM6E12Y2nwXEO4j7zCsvBxsFrUjEa2QJFAeIvf55yn/5Re1wxAt4JwjzXsrv63aRlVKFVUpNTNwm/PNVKVUYS4wo9FoCDs3jLzv8ijdUUp1ejUZ72Tg5eNF0OgTd8ml/zud7C+yHY/Dp4VTvr+cvNV5mDJN5K3Oo/xgOWHnhtW5fnVGNSVbS4i4KAIAnw4+oIHCjYWU7S7DlGXC0NXQbM8/ThPQbG0LN5S0Eb6/Q+0oRCskY6A8QPGXX1L47ntqhyFaSNC2I/iN9KbSq3m+NVclVZH8dLLjcfaKmkQoeFww0ddGEz4zHLvZTuZHmTUTaXYzEPuPWKc5mswFZqcxT349/Ii5MYacL3PIXZWLvr2emBtj8Ovm3AOlKAqZ72cSuSgSL5+a73deei86XtORrI+zUCwKHS7vgHdI85Ux47ITmq1t4aZ2fQzhPWHcrWpHIloRmYm8lavYupXUq68Bi3RBtyWrbxjEhyEyTsfVOhuj+H7/ZrXDEGrw0sHS1dDJHe7SEK2BlPBaMUtODhm33S7JUxs0/qgsR9EcpnjXLSmKNsJuhf9dBZWFakciWglJoFopxWYj885/YCsqUjsUoYKgbUfxVaQC72pT89LVDkGoqTSjZo4oKcyIRpAEqpXKf/0NKrdvVzsMoRKlrJwFxb3UDsOjtPcNZ0D6XrXDEGqLXwd/vKh2FKIVkASqFarYspX8t95SOwyhsglSxnOpKYYoNI2aEVR4vA2PQ8omtaMQbk4SqFbGWlRE5t13g70ZJ8IRrYKU8VwrrihH7RCEu/h7PFRFgdqRCDcmCVQroigKWffehzVH3uhF85XxrOVWDt1yCHOe2eVtn63U11LJX+P6mfaD9IEMT9nl8nZF62Xx8uHfa7aoHYZwY/L1tRUp/OBDyjduVDsM4UYmHNPy0UjXtpn3fR4BgwPQt9MDsH/p/jrHRC2JInRKaJ3tphwTCQ8mgAb6vtnXaV/F4QqyVmRhyjChC9HR7rx2ddqwVdjI+TKH0h2l2Cps6NvpiVwYScCgmskt289pT9JTSYRMDHGad+psTTR2Qmev+zxF25QWPYuL0+aTm6Pg0yGZK8bGqh2ScEOSQLUS+ell/Jnema4dYtFlJasdjnATQVuP4jtCR7XGevqDG8FutlP0WxGxy2Odtne8uiP+A/wdj7V+dZMXxaqQ/lY6fj39qDzmvFadOc9M8gvJhE4MJfr6aCqPVZL1URbaAC1BI2pmMLdb7SQ/l4w2QEvMzTF4h3hjKbTg5Xuio9w3xhfvcG+KNxUTNsV1Uw5MLSt1WVui9VK8jXzW7lbuix/g2PbED4cY0y2MnhEyQ71wJiW8VsBmsbPuvYOkZyhsHXofFRMXqh2ScBNKWTnzXbg2XtneMjRaTZ016rR+WryDvR0/Xvq6bx05q3LQR+odCVFthRsK0Yfp6XBpB3yjfAmdGErw+GCnclzxb8VYy610vrUzxh5G9OF6jD2NGDo5L9sSOCSQks2uWwDWoDMwNknuaG3rqsL6cYX3s9yXOMBpu8lq59YVuzBZbSpFJtyVJFCtwKavEyjMrACgusLKFs140hY9g93XqHJkwh1MOOq6juTKI5UYYuuuM5f530wO3XyIhIcTKFxfWLPAby3lB8sp2VZC1JKo+tuNr8S/v7/TtoABAVQlV9UsDgyU7i7Fr7sfmR9ncujWQxy7/xi53+XWuZahq4GqxCrsFtfcSHFOQFd8rNUuaUu0TgdjFjE8515+Kwyud//h7DKe/vFIywYl3J4kUG4u/XAhe9an1dl+LMvInvNfwNx3jApRCXcSvPWYy+7GM+eb0QU7t9X+ovZ0WtaJ2LtiCRoZRNZnWeR9n+fYby23kvFOBtHXRDc4LslaYkUb6LxPF6gDW835AOZcM6XbSlHsCrHLY2k3ux0FawrI+y7P6TzvEG8Uq4K1xDVly7jKKpe0I1ofuyGUV9s/ysxjs6mwnnpM3ft/JfHb0bxTHiPaFkmg3JipysovHx6ioalpigqsbOq4hIK5d7VsYMKtKGVlzHNRGU+xKHh5O78ttL+gPX7d/TB0NhB+XjgRF0aQ/+OJ0lvm+5kEjQ7C2OvUPaIajcbpsXLyC1upSao6XtkRQ6yB4NHBtJvdjsL1zktraLxr2rGbz74HytvLm4nJO8+6HdH6lEaMYo71aZ5P7dao4xUF/vHFHgor3O/uVKEOSaDc2G8rjlBeZDrlMTaLnT3FsRxZ9Aa29p1aKDLhbiYe83ZJO1p/LbbKU4/1MHQ3YK+yO3qAyg+Wk78mn/1X7Wf/VfvJeC8De5Wd/Vftp+i3mqWGdEG6Oj1GtlIbaEHnX9PjpQvWoY/Uo/E6kWj5RPlgLbFit55IlmwVNfHpAs6+121UYDf8q2UAeVuiaLT8FXMdQ1JvYV9Z04ZB5JaZ+Nc3cremqCF34bmpY9tzOLq18fM9ZWQpFI68nwGlv+D/2+fNGJlwR8Fbj+EzXItJc3YDXQ2dDRT/VXzKY6pTqtF4a/Dyq/n+1fWBrlCrM6h0Zyn5P+TT9Z9d8Q6pSez8uvtRtrvMqZ3y/eUYYg1odDUJk18PP0o2laDYFUcSZco2oQvW4aU78V2vOr0aXajOJQlUnAt6sUTrYfOP4mH9cj46Vv9Yvcb4fm8WC4bnMaFnOxdGJloj6YFyQ9UVFn5febTJ51WVW9mqnUj6oqdRfOoOBBaeSyktdUkZz7+/P9WZ1Y5entJdpRT+Wkh1ejWmXBOFGwvJ+TKH0EmhjlKfb5QvvtEnfrxDvEEDvtG+aI0140pCJ4dizjeTtSKL6sxqin4roui3IsJnhDuuHTo5FFuFjaxPsjBlmyjbXUbe93l15oqqPFqJfz/nAelnwkvjxeSUPWfdDsBvKVZmr6gk6vkyNA+X8vVhi9N+RVF46Ndqop4vw/B4KZM+qOBA7umT3S8PWuj7ejk+j5XS9/Vyvjrk3O4ney3EvFhG6NOl3LXWeSB8crGdnq+WU2qS5WkAcqPimFzxGB9lnnny9Ld/fbOfaovcldfWSQLlhjZ/k0hVmeX0B9ZHgaNZ/uye/TLmPqNcG1grtb2ykpvS05gYH0/fI4f5ucy5J0RRFF7Lz2NifDxDjh7hitQUjplOXToFWFtWyqykRAYdPcKspMQ67X5XWsKUhHhGHzvKs7m5TvsyLGbOS0yg3Oa6N+GJx/Rn3YZvjC+GWAMlW2umCdBoNRSuLyTxsUTi/xlPwboC2l/YnsiFkU1qV99OT+zyWCoOV5DwrwRyv82lw6UdnKY80Ifpif1HLFVJVcT/M57MTzIJmxZGu1knvunbzXZKd5YSOrHuJJ5NNTiwK2HlrhkUXGFWGBThxWszfevd/8yfZl7YZOa1mb5su9ZIpL+GaR9XUnaK5GZTmpVL/lfF5QO92XODkcsHerPgf1VsSa8pheZX2rnmuyqem+bLT5cZ+XCPhdVHT7xv3Li6iqem+hDoo2noEm2CovXh++g7GJl4NalV9f/7NFVyQSVvbIh3SVui9ZISnpvJTSnl4O8ZZ91OUb6FTdFL6d9zHGHfvOCCyFqvSrudXj6+XBgUzG2ZdX+37xYW8mFREU9EdiBWr+etgnyuSUvjh65dMHrVf2fO7qoq7szM5Jbwdkz19+fn8nKWZ2bwcafODDIYKLJa+Vd2Nk9EdiDa25sbM9IZ6efHRP+anpOHc3JY3q49/lrXzaYd4qIyXvsL2pO9MpuQiSEEDAwgYGDTJhAMGR9CyPiQOtuNvY10f7j7Kc/16+5Ht381PKi36LciDF0NdeapOhNTbWefcP7tvB7enNfj73Foznf1KYrCS1vM3D/eh4v61Bzz4VwDEc+V8ek+C9cPrz+Ol7aYmdZNy33jfQC4b7yWjSlWXtpiZkW0jsQihSAfDZf0r2lzchctB/PsnN8TPt1nQa/VOK7XVpmDu3O77VZ+iA8//cFN9NbGROYM6Ui3dmffGypaJ+mBciOKXWHjiqMoLupxt1ns7CnpxtHFr2NrF+2aRluhCf7+3NauHdMC6iYCiqLwUVEh14eGMS0ggB4+PjwZ2YFqxc73pQ0PLv6oqJAxRiPXhYXR1ceH68LCGO1n5OOimjvG0iwW/L28OC8wkAEGAyP9/Ig31/RqfV9agrdGU288Z0MpLeXi0rMv4wUMCiBkUgiWojPsBW1GGp2GqMvOvgQDEJd+0CXtnE5SsUJ2ucK53U58X/XRaZgYq+Ov9IaT3U1pNs7t6vwdd3o3HX+l1ZzTI9SLSovCriwbhVUK2zJsDIzQUlil8K8N1bx2nmt6W1qrpJi5jCl8gB/yXJ88AZhtdh74WgaUt2WSQLmRg39mkpvs+juC0jNh26gHqBg/z+Vtt3bpFgv5NhtjjSfuxtF7eTHcz4/dVQ3PD7S7qopxfs538IwzGtl1/JzOej3VisLB6mqKbTb2V1fTy8eHYpuNV/Pz+Wf7iGZ5PpNcUMYDCD83HH2Y63poXCV0Uig+HXzOup0+AbFEFaW6IKLTyy6vGage4e9cSoswahz76j9PIcLf+S06wt+L7PKab1ghBg0fzjWw5OsqRv6nnCWDvJneXcc/1lZzy0g9ScV2hrxdTv83yvnfQfdLhpuL4hPABx0eYPKxBRSYm7cH7q+EAr7ald6s1xDuS0p4bqK63MLmrxObrf3KcitbdJPptWg4UaseRmOSyQMB8m0140nCdc6ltHCtlkxLwxM15luthJ10TphOS/7xMU1BWi1PRnbgvqwsqhU7FwQGco7Rn/uzsrgsJIQMi4VlGelYFYVl4eFMDwh0yfMJ2XoM/TAt5rMs43m6OE3Ll11OHomkKHW3NfWcC/t4c2GtMt2vyVb25dp4baYv3V8pZ8XFBiL9NYx8p4IJnbW0N3r2d+bK8EFcWX4jW5Jc8/fUGI+vPsSU3hEEGdp2ubQt8uy/plZk09cJVFc087dEBY5kBbBn9kuYe49o3mu1MhpOnuSxMR9upz5nakAA33Tpwk9du3FzeDu2VlZwzGxiXlAwd2Zmcl/7CF7u2JEHsrMpsLpmVm2lxDV343m6qVktNwA48ngv0t89R3/Lrazbw+R8Xt0eqtwKe52erL+ZrAo3ra7m7VkG4gvtWO0wMVZHr3AtPcO82HKKcmFrp6BhT6fLGZb1D7YUt1zyBJBfbubpNYdb9JrCPUgC5Qayk0o4+Gdmi12vMN/K5pirKLzgjha7prsK19Z0wuadlMAU2Gx1epicztPpyD/pnEKrjbAGBoWb7XYeycnhoYhIUs1mbCiM8POji96HWL2evdWu6xGcFO9+pTd3EmvsSLfcpk8Tcqa6BGuI9NewLvHE68VsU9iYbGVsdMOvsTExWtYlOic9axOtjI2p/5xHfzNxXncdQztosdnBWmsNQYsNbB46m4HdL5zn2z3OnKPnUWVz3U0ZTbFiayo7U4tUubZQjyRQKlPsCr+tONrgci3NxWqxs7u0e80A8/COLXtxNxLt7U24VsumigrHNrOisL2yksGGhufSGmww8FdlhdO2PysrGNLAOW8WFDDeaKSvry82wFrrTgGLorj0wy1k6zH0ijofJK1BnPfZT4FwsnKzwu5sG7uzaxKepCI7u7NtpJbY0Wg03D5KzxO/m/jqkIX9uTaWfl2Fn7eGxQNOlH2WfFXFfT+fmMvptlF61iZYefoPE4fzbTz9h4mfE23cPqpugnwg18bKA1YemVwzPqx3uBdeGg3v7jSz+qiFw/l2RkR53muiKHIcM81P8VparKpxKArc/9V+bHYPzVJFvWQMlMoO/J5BXmrZ6Q9sJumZUDjmXwws/Am/P1epFkdzqrDbSTWfWL8qw2LhUHU1QVotUd7eLAkJ5d+FBXTW6+ms1/PvggJ8NV7MCjxRCrg3K5P2Oh3L27UH4PKQEJakpvJOQQFT/P1ZX17O5ooKPu7Uuc71j5lM/FhWyqrYLgB01evx0mj4sriYcJ2OJLOZAb6uu2NKKSnl4tIBrAg65LI2PUlcrusHj2/PtDH5w0rH4+VrTYCJKwZ588FcA3eP01NlVbjph2qKqhRGRWtZe7kfAbXmaEotseOlOfGddmyMjs/mGfjnehMPbDDRLdSLlfMMjIp2fttWFIXrvq/mxek+GPU17Rm8NXww15dlP1RjssJrM33pGOg535cVLx0bO17HlfHjUBT3mOfqUFYp7/+ZxDXju6odimghGkVx1U3zoqmqysx88uBmTJWuGf9yVjTQK6K0ZoC5ufr0x7ciWysrWJqWVmf73MBAnugQhaIovF6Qz+fFxZTa7Qz09eWBiEh6+Jy42+uK1BQ6envzRIcTt9D/VFbKK/n5pJnNdNLruS287lQJiqJwWWoq14aFMcn/xMDlX8vLeTQnG7OicFt4O+YFB7v0ORdPG8Z1w10zy7YniTS0Y+3BnWhaustXuIw1MIZ/am/ns6wOaodSh1Gv5ec7J9IhSFaCaAskgVLRLx8d4vBfWWqH4SSsnY4+e/6D/uh2tUMRZ0ETHMSlN1TL3XgnWRwykPt2fq92GOIMZXacwcUZC8mqdt9xftP7RfD25cPVDkO0AM/p021lshNLOLzJvZIngII8K5tjr6Zw9u1qhyLOglJcwkWlPdQOw+3EFTZ+gW7hPhSdgVUd72JswhK3Tp4AfjqQw4bDuac/ULR6kkCpwG5X2LjiSIsPHG8sq9nO7rIeHFv8GrYw9+smF40zOf7sJ5z0JCH6IIal7FQ7DNFEptBeXOf7LMsThqgdSqM9veYwUtzxfJJAqeDg7xnkp5WrHcZppWVq2Db2ISrHzlU7FHEGQrfGy914tUw0dkKrSEmzNTkaM5+RefezLt/1d042p8PZZfywL1vtMEQzkwSqhdksdrb/kKx2GI1WWWZls880Mhc+jl0vPRqtiVJcwoVSxnOYWlrcLO0WVNpp/2wZycUNL8uilnmfV/LCJpPaYTSZ4hPEvyMf4txjF1JiaZ03i7/081HsMq2BR5MEqoUd+CODihLz6Q90Jwoczg5m35xXMPcYqnY0TVZss3FO/DEyLO73e789I4MPCgubrf3J8W17Qdm/+en8GJPUPDdGPPmHmdk9dcQG17ydah4urfPz1vb6X3vxhXYCniwl+CnnNTBXHbIw7eMK2j1bRuCTpYx5t4Kf4p3v1j2Qa+PizyuJfakMzcOlvLS5bqL0r4k+PP67mVJT6/kgL28/jIt5hieSW/eM+sdyy/lmT4baYYhmJAlUC7JZ7Oxck6J2GGesIM/K5i7XUjTrVrVDaZJ/FxQwyd+fjt41g0/7Hjlc5+ez4vpnEU4xmxl+9CijjtWduXpbZSXzkpMYfPQI5yYmNNgGwA+lpfQ9cpibM5wXHr0xLIy3C/IptzVPaSlsm5TxAMYHdEFvc31PTJVF4d1dZq4Z6rwO2vtzfMm609/xc8WguuukWWwKi76sZHynuj0sv6XYmNZVxw+L/dhxnZHJsVpmr6hkV9aJ10mlBboGe/HUVF8iG1jeZWCElthgDZ/sdf/FhBWNF9tjrmRY+h3sLAk4/QmtwMs/H8Nqc7+eSeEakkC1oFbZ+3QSq9nOrvJexC9+FXtopNrhnFa13c6qkmLmBQU7bX88MpKN3bo7fuYGBtU516Io3JWVyTC/unO6pJvN3JCexjA/P77sHMt1oWE8kZPD2rLSOsdmWCw8m5fLsHpmKe/l60tHb2++L617nisoRcXMLZMyXlxF8yye/WO8FZ2XhjExzklQsK+GSH8vx4/Bu26C88/1JnqHa1nQr24C9dIMX+4e58OIjlp6hGl5Is6XHmFefHf0RC/UiI5anj3Xl4X9vfE5RY58QU9vVux37wTKZozg8bAnmXdsGia753wsJRdUsmqn9EJ5Ks95pbo5q8XWqnufTpaa6cW2cx6mcswFaodySr9XVKDVaOosyxLgpaWdTuf48fWq+6fwSn4eXfR6ZgTU/Ta8sqSYDt7e3Nc+gm4+PswLDuaioGDeP6kcZ1MU7snK5OawcGK861+tfbJ/AKvrSbxcZUobL+PpvfRMSN7RLG3/lmJjeFTd187NP1QT/kwZI/5TzlvbzdhPuiNrfZKVLw5aeH1m4/5t7IpCmUkh1ND0WbdHdvRia4YNk9U9y3gFHSZybtUTvJMeo3YozeKV9cewSC+UR5IEqoUc+D2z1fc+nayi1MoW3+lkXvKY2w4w315VSb96lkl5PDeHsfHHWJCSzGfFRXU+4DZXVPBTWRkPtI+ot93dVVWM9TM6bTvHaORAdTWWWm29UZBPiFbLxaeYaXyAry/7qqsx25vnTTZsazw6pe3+qY8K7IrR1DzLJSUX24nyd/7dPjrZhy/mG/h5iR8L+3lz59pqnvj9xN9+QaWdpV9X8cFcA4E+jUuInv/LTIWFenurTqdjoBcmG2SXu1cCpWj1rIu+leHJ15FQ6bkzd6cXVfHZtrorIYjWr3Xe3tDKWC02dv3kOb1PtSkKHM4JIX/uK/TZ8SbeCbvVDslJhsVCe53zy/zW8HBG+xnx0WjYXFnBs7m5FNts3BAWDtQMOv+/7Cye6RCFv7b+2ki+1UqY0XlfmE6L9fj57XQ6dlZWsqqkhFWdY08ZY4ROh1lRyLPZ6FhPT9jZUoqKubCsH18EHnF5263BVFPzffuvsir46pyToH9OOPFlYnBkzWvkkd9Mju3XflfN4gHeTOjcuLffFfssPLTRxDcL/WhvbPrrw3D8MpUW90mgLEFduIvb+Dq+vdqhtIjX18czf1g0vt4yHtGTtN2vpS3IE3ufTpafa2VTt+spOn+Z2qE4MdkVfDTOH3A3hIUz2GCgj68vV4aGcXN4OO/VKr39KzuLWYGBDPfzO2XbGpzbrd2JVWG3cU92Fg9HRBKiO/UHpc/xpKm6mXqgAKbEe+43/FPRarRMStndbO2H+2koqj51YjI6WkupCXLKa/591ydZee4vM7pHStE9UsrV31ZTYgLdI6W8t8v5fWLlfgtXf1vF5/MMTO16Zt93C6tq4mtndI9Fd9OiZzGu+CG+zmkbyRNAdmk1n25x/SLWQl3SA9XMPLn36WRWs51d5r50WvwqXdY8htYNls0I1mopPc34g0G+BsrtdvKtVsJ1OrZUVrKhvNwxnkkB7MCAI4d5KDKSi4OCCdfpyLc631ZeaLOhO37NeJOJDIuFZbXuuvs7igFHDrO6S1c66WvuCiw5fgdeaAO9Xa4QvjUe3RAvrJq2NRZjSGBXQhN/ab72I7X89zR3uO3KtuOrqxlYDrDpaiO2WjnXN4etPP2nib+uNtIx4MR32hX7LFz1bRUrLjZwfs/6x881xv5cO9GBGsL91P2+rHgb+azdrdwXP0DVONTyxq8JLBrZCYNeeqE8hSRQzawt9D6dLDXTi4JzHmFgzncYtqi7cGsfXx++O80dbodMJnw0GgKP9wR92qkztdOMX8rLeLewkE87dXaUAwcbDGwod55N/s+KCvr5+uKt0dBVr+eb2C5O+1/Oz6PCbuf/2kcQWWtAebzJRKROd9qeqrPRVst4cbbmfYub3k3Hfb+YKKpSCDFo+O6IhexyhTExWgw6DRuSrdy/vprrhurxOV7q69PO+QN0e6YNLw30b39i+4p9FpZ8XcXLM3wZHa0l+3jvlUGnIeh4Ima2KRzMsx//f8goVdidbcNfr6F76Ilk6fdUG+eeYe+Vq1SF9ef6qpv4LTFY1TjUlF9u4sNNydwwsZvaoQgXkQSqGbWl3qeTVZRa2ex3Hr0vGUXEl4/iZVUniTzHaOSlvDxKbDaCtFo2lJeRb7Ux2GDAR6Nha2UlL+fnMT8oGP3xBKqbj/OA+P3VVXgBPWptvyQomE+Ling6N4d5QcHsrq7iy5JinouKAmrKcj1OaufvBO3k7TuqKhlrdB6Q3hymxBv4ovXNg3pWpqYdaNb2B0RoGR6l5fMDFq4frsdbq+GN7SaWr7VjV6BriBePTPJh2cimLYD79g4zVjss+6GaZT+c2H7FIG8+mFtTjs0sUxjydoVj33ObzDy3yczEzlp+XVrzeqq2Knx12MJPl526HN2cDsYsYn7STCqs0vPy9sYELhvdGX8f+ej1BBpFVjxsNnvWp/HH58fUDkN14e119Nn+Ot6Je1W5/qKUZOYGBXFJcAi/V5TzYl4eqWYLCgrR3nrmBQexKDgEnab+MSJflRTzVG4uW3o4z4y8rbKSp3JziDebaa/TcXVoKAuDQxqM4/+yMim123mtY7Rjm8luZ3xCPP+JjmFQPfNEuZImJJhF11e2mTJev8AufLZnY7Nf54djFv6x1sT+m4x4NfAaUsvrW818c8TC2subP0E/md0QyusBd/B8qvS41HbntJ7cEidzs3kCSaCaidVi47//3NTmyncN8fbxYoB2H8E/vNHi195YXs6zebl8G9vF7T7gPi0qYn15Ge/EdGqR632+rB//ayNlvFsD+3Htnh9b5FovbzZxUR9vYoLc676cf++o6ZHqFd6yvT+lEaO4tPAa9pW1fOLm7gJ9dfxx7xQCfc98XJtwD+711+5B2uLYp1OxmOzsrOxHwuJXsIW07N03E/39uSQ4mJyTBn27A51Gw/0R9c811RziEtrO3XhxWS3X+3vbaB+3S54Arhumb9HkSdFo+SvmOoak3iLJUwNKq618sT399AcKtyc9UM3AZrXz8f1/SQLVAP8gHQOyvsGw9YfTHyxcShMawqLrKjy+jNfF2JFv929SO4w2xeYfxcP65XyUGaV2KG6vS7iR9XdORONmPeKiadzvK5MHSNiZK8nTKZSXWNlsPJ/sBY9g1zVtcK04O0phEXPKPX/8xVRdqNohtCm5UXFMrnhMkqdGSsqv4Pdj+WqHIc6SJFDNYP9vsnjk6SgKHMwN48DFr2Dp0l/tcNqUuDYwqWZcbtu8+7WlKVofvo9ezsjEq0mtattrLjbVx5vlNdraSQLlYoWZFWTFl6gdRquRl2Njc89llJx3g9qhtBnttiWixXNLBx0M7eiXuV/tMDyeObg7y/ye5eb44WqH0iqtP5xLRnGV2mGIsyAJlIvt/116n5rKYrKzo2oACYtfxh4UrnY4Hk8pKGROmeeW8ab4RKodgsdLipnLmMIH+CFP/l7PlM2u8In0QrVqkkC5kNVs4+iWbLXDaLVSMnVsn/Q4VSNmqB2Kx5uaoN7Eis0tzg2WEPJUik8AH3R4gMnHFlBgltvwz9bn29MwWz37hg5PJgmUCx3bnoOp0v1ulW9NykusbA6YTfaCh1C0Mltvc2m31TPLeKE+wQxN3aV2GB6pMnwQCzXP8lBSH7VD8Rj55WZ+2JeldhjiDEkC5UL7f8tUOwSPoNjhYG479s97DUtsP7XD8UhKQSEXeGAZb5JfDFrFpnYYHkVBw55OlzMs6x9sKQ5UOxyP89GmZLVDEGdIEigXyUstIzf51IvWiqbJy7GxpfctlMy4Tu1QPNLURM+b6DCupEjtEDyK3S+c59s9zpyj51Flk7XsmsPO1GIOZMqNR62RJFAuIoPHm4e52saO6kEkLn4Ze2CY2uF4lIitCR5VxjPq/BidvEPtMDxGUeQ4Zpqf4rW0WLVD8Xgfb5LB5K2RJFAuYK62cmyrDFxtTsmZOnZMeZLqYeeqHYrHsOcXMqu8u9phuMz4gC7obSa1w2j1FC8dv8bcxNCUmzhc7rk3G7iTb3ZnUlJlafbraDSaU/4sXbq02WPwJJJAucDRLdlYTDLuormVFVvYFDSHnPkPygBzFzk33nPKeHHllWqH0OpZA2O4N+gZlh47B0XxnN5Jd1dlsfG/Hc2/Pl5WVpbj56WXXiIwMNBp28svv9zsMXiSFk2gsrOzueWWW+jatSs+Pj7ExMQwe/ZsfvnlF5ddIzY2lpdeesll7TWGDB5vOYodDuS158C817B26q12OK1ehIdMqumj9WFCipTvzkZmxxmML32UlVkyj5Ya/rs5heZemjYyMtLxExQUhEajcdr26aef0q1bN/R6Pb169eLjjz92Ol+j0fDmm29y3nnnYTAY6NKlC1988UWzxuzOWiyBSk5OZtiwYaxfv55nnnmGffv2sWbNGiZPnsyyZctaKgyXy04soSCjXO0w2pzcHBub+95O6fRr1A6lVfOUMt7ogK74meTv8EwoOgOrOt7F2IQlZFXL2pRqUXt9vK+++orbbruNO++8k/3793P99ddz5ZVXsmHDBqfjHnjgAS6++GL27NnDZZddxqJFizh06JBKUaurxRKom266CY1Gw9atW5k3bx49e/akX79+LF++nM2bNwOQmprKnDlz8Pf3JzAwkAULFpCTc2JsUUJCAnPmzCEiIgJ/f39GjBjBzz//7Ng/adIkUlJSuOOOOxw13eZ2QNa9U4252sZ20xCSFr+EPUAWjz1T5yb4qx3CWYuTEvoZMYX24jrfZ1meMETtUATwyRb1BpM/99xzLF26lJtuuomePXuyfPlyLrroIp577jmn4+bPn88111xDz549efTRRxk+fDivvvqqSlGrq0USqMLCQtasWcOyZcswGuuOuQgODkZRFObOnUthYSEbN25k3bp1JCQkcMkllziOKy8vZ+bMmfz888/s2rWL6dOnM3v2bFJTUwFYtWoV0dHRPPLII46abnOqrrAQvyO3Wa8hTi8p05sdcU9RPXSa2qG0ShGtfFJNrUbL5OSdaofR6hyNmc/IvPtZly9fPtzFhiN5lFY3/2Dy+hw6dIhx48Y5bRs3blyd3qUxY8bUeSw9UM0oPj4eRVHo3bvhMSs///wze/fu5dNPP2XYsGGMGjWKjz/+mI0bN7Jt2zYABg0axPXXX8+AAQPo0aMHjz32GF27duXbb78FIDQ0FK1WS0BAgKOm25yObs3BapFp+N1BWbGFzcEXkjP/ARQvma+mKez5BZxf1nrLeMMCuxFcWah2GK2G4hPEvyMf4txjF1JikZsx3InZamfdAfXu6D65aqMoSqMqOS1R7XFHLZJA/T0w7lS/5EOHDhETE0NMTIxjW9++fQkODnZktxUVFdx9992O7f7+/hw+fNjRA9XS4nfI1AXuxG5XOJAXycEFr2Pp1EvtcFqVc5Nabxlvikzw2Gjl7YdxMc/wRHJPtUMRDfhurzo3JfXp04c//vjDadtff/1Fnz7OS/f8PeSm9uNTdY54shb5+tGjRw80Gg2HDh1i7ty59R7TUKZbe/tdd93FTz/9xHPPPUf37t0xGAzMmzcPs9ncnOHXq6LERHaCzB7rjnKybRT1u4OBvbcSuPY9tcNpFSK2JqEZAK3tznUNGuLS9qsdhttTNF5sj17KZQlTMNll9hp39md8PsWVZoL9WnZA/1133cWCBQsYOnQocXFxfPfdd6xatcppnDHAF198wfDhwznnnHP45JNP2Lp1K++++26LxuouWuQvKTQ0lOnTp/P6669TUVFRZ39xcTF9+/YlNTWVtLQ0x/aDBw9SUlLiyIB///13li5dyoUXXsiAAQOIjIwkOTnZqS29Xo/N1vwDShN35dHMd5yKs2CusrHdPIzkxS9i9w9WOxy3p+TlM7ui9a2N1y8wlshiuZHjVGzGCB4Pe5L5x6ZK8tQKWGwKa/Znt/h1586dy8svv8yzzz5Lv379ePvtt3n//feZNGmS03EPP/wwn332GQMHDuTDDz/kk08+oW/fvi0erztosb+mN954A5vNxsiRI/nyyy85duwYhw4d4pVXXmHMmDFMnTqVgQMHcumll7Jz5062bt3KkiVLmDhxIsOHDwege/furFq1it27d7Nnzx4WL16M3e48Bik2NpbffvuNjIwM8vOb75ZQGTzeOiRm6tk57Rmqh8SpHYrbOzex9ZXx4vCciUCbQ0GHiZxb9QTvpMec/mDhNlqijLd06VKKi4udtt14440kJCRgNps5cuQIl19+eZ3zoqKiWLt2LdXV1SQnJ7Nw4cJmj9VdtVgC1aVLF3bu3MnkyZO588476d+/P9OmTeOXX37hzTffRKPR8PXXXxMSEsKECROYOnUqXbt2ZeXKlY42XnzxRUJCQhg7diyzZ89m+vTpDB061Ok6jzzyCMnJyXTr1o127do1y3OpKDGRFV/cLG0L1ystsrA55GJy5/1TBpifQsS2JDStrFd1auYRtUNwS4pWz7roWxmefB0JlQa1wxFNtDmxkPxyWZbI3WmU5p761APt+zWd3z47qnYY4gxERHrR648X0KUfUzsUt/TRLb353j9e7TAapZt/NF/v+0vtMNyOJagLd3EbX+e0VzsUcRYendOPy8fEqh2GE41Gw1dffdXgWOa2RgriZyBhl5TvWqucbDtbBtxJ2dSlaofilqYnBqgdQqNN0YaoHYLbSYuexbjihyR58gA/qTidQUP+nq9R1JAEqomqys1kHpO771ozU5WNbdYRpCx+Abt/kNrhuJXIVlTGm5qTpHYIbkPxNrIi6j7Gxy8m1+StdjjCBbYkFVBSpc6kmqJxJIFqouS9BSj2VvIJI04pIdOHndOepXrwZLVDcRtKbj4zK9x/Us2OfhH0zTqodhhuoSqsP1d4P8t9iQPUDkW4kMWmsOGwVDvcmSRQTZS8T73FHoXrlRZZ2Bw2n7yL/08GmB83I8n9y3iT9VKiAjgQs4jhOffwW2Gw2qGIZrD2YMtPZyAaTxKoJrBZ7KQdlCUjPI3dprCvoCOHFryGtaP79740t8htyW5fxpta0LY/WOyGUF6NeJTzj82mwiqJv6faeCQPk1UWynZXkkA1QfrRIiyy6rvHys62s2XQPyiLW6J2KKpScvI4r7Kb2mE0KNQnhCFpu9QOQzWlEaOYY32a51Pc999IuEaF2caf8VL1cFeSQDVB8l55IXs6U6WNbbZRpCx+Hrux7Q4wn5EYqHYIDZrsF42X0vYW8VY0Wv6KuY4hqbewr0wmEG0r1rrh3XiihiRQTSDjn9qOhExfdk1/FtPACWqHoooOblzGiytpe2V0m38UD4Y8zeJjk7Ap8rbdlvx8KBeZrtE9yV9iI+WllVFeKDPDtiUlhRY2tVtI3kX3odSz0LUnc9cynr+3kdHJO9QOo0XlRsUxueIxPsqMUjsUoYL8chNHc8rVDkPUQxKoRkrZV6B2CEIFdpvCvsJoDi98A2tUV7XDaVHuWMYb798Fb5tZ7TBahKL14fvo5YxMvJrUKl+1wxEq2poknz/uSBKoRsqUte/atKwsO1sH303ZlMvUDqXFdNiW4nZlvKkVbeObuDm4O8v8nuXm+OFqhyLcwNbkIrVDEPWQBKoRFLtCTqLMPt7WVVfa2GYfQ+qi57Ab3a93xtWUnFxmuFEZz0frwzlJnl++S4y+kNEFD/BDXrjaoQg3sS2p7Y37aw0kgWqE/IxyzNUyfYGoEZ9lYNf05zANGK92KM1uRpL7JIpjArrhZ65QO4xmo/gE8EGHB5gSP59CiyzHIk7ILq0mpcBzX/utlU7tAFqD7ATpfRLOSgotbGq/iP4XjSPsq6fReOhdMlHbUtD0A8UNxtDHVXvuumCV4YO4svxGtqiQsFan7ad0y5eYcxKwlRfS7sL78es5xrFfURRK/vyU8j0/Ya8uR9+hJ6HTbkTfrnODbZrzUij54xNM2fHYSnMJmXItgSPmOB1TfmADxRs/RLFU4z/wXEImX+XYZy3JIWflA3S44iW8fPxc/6RboS1JhXQOk+kr3In0QDVClox/AiA+cy9v/Xg///fxAm5+O449SX847VcUhdXbP+T/Pl7AHe+cx0vfLierMPm07e5K/I3HVl7J7f+ZwWMrr6zT7rZjP/PP/y7k7g/m8tWmt532FZRl8/BnS6hSoWfCblPYWxjD4YWvY+vQpcWv3xKU7Fymu0EZT6fRMTllp9phuJyChj2dLmdY1j/YUqxOb59irsa7fVdCp95Q7/7SLV9Suu1rQqfeQOSSF9AaQ8j9/AHspsqG27Sa0AVHEjLxCrTGkDr7bZUlFK55lZDJV9F+wSOU7/+FyoRtjv0FP71ByMSlkjzVImU89yMJVCNkSQ8UACZrFR3DurFg3C317v95z2ds2Ps/Foy7hbsueoNAvxBeXX031eaG32gTsw/w/s+PMqLnNO6d929G9JzGuz8/QnLOIQDKq0r4dOPzXDj6epbNfIotR9eyP2Wz4/yVv7/EnJHXYtCr980sK0thy9B7KZ+0WLUYmtN5yepPKDossCtBlZ41kNbuF87z7R5nztHzqLKptxyLodtwQiZcjl+vsXX2KYpC2fZvCBpzCX69xqJvF0v4+cuxW0xUHNrYYJs+HXoSMvkqjH0ngrZuOdJanI3Gxw9jnwn4dOiJb6eBWPJTAag4+Csara7eeNqyrcmSQLkbSaBOo6ywmvIimf8JoF+nUcweeRWDu9Yd+6MoChv2rWL60MUM7jqeqNAuXD75HizWarbH/9Jgm7/uW0Xv6GFMH7KYyJBOTB+ymF5RQ9mw70sA8suy8NUbGdZ9Mp3b96Zn1GCyi1IA2HbsF7Re3vXG09KqK6xsZRxpi5/FbvBXOxyXitqWonYIxFk9662qKHIcM81P8VparNqhnJK1JAdbRRGGLkMc2zQ6b3xj+mPKOHTG7epCO6JYTDVlw6oyzFlH0beLxVZVRvHvnxA6rf7esLYspaCSnNJqtcMQtXjWu1IzyEooVjuEVqGgLIvSykJ6R5+47dpbq6d7h0Ek5hxo8Lyk3INO5wD0iRnuOKd9UEcsVhNp+ceoqC4lJe8IUWFdqaguZfX2D1hwTv29YWo5lunH7pkvYOp/jtqhuIySlcP0SvXmwNKgIS5tn2rXdyXFS8evMTcxNOUmDpe7f3nKVl7T6+flF+y0XWsMduw7E1pff8LPv4P8718g+6PlGPtPwdB1GEUb3iVg2CysJTlkvn8rme/eRMXhP07fYBuxRcp4bkUGkZ9GVryU7xqj9Hh5JcDgPN4hwBBCYXnDazmVVhbWe07Z8fb8fAK4fPI9fLThaSxWEyN7TqNvzAj+++uzTOw/l4LSbN5e8wA2u5WZw5cwpOtEFz+zpisusLA5cjH9eowl7OtnPWKA+XlJwfzUT51rDwjsQvukX9W5uAtZA2O4X3sHK49Fqh1K0508E7+i1N3WRH49x+LX80SZrjp1L5a8FEKn3UDmv68jfPZdaI0hZH20HN+Y/miNwWd1PU+wLamQCwbJjPTuQhKo05DxT02j4eQ3VaWebSedU+eNWKH2KYO6nMOgLid6dI5m7iazMIkF427hoc+WcGXc/QT6hfLsV8vo3mFgnYRMDTarwt6izkQtfJ3uvz6DLitZ7ZDOSsftqaBSAhWnGNS5sAtldpzBxRkLyarWqx1Kk2j9a/6W7BVF4B/q2G6rLHFpQqNYLRSufZOwWXdiLcpCsdvw7TQAAO/QjpiyjuDXfZTLrtdabZUeKLciJbxTMFdZKcxoGzMfn61Av5o32tIq5z/wsqpiAk7q/nc+L5TSynrOaSAJstjMfP77yywafzt5pRnY7TZ6RA0iIjiG9kHRjsHn7iIzS2Hr0PuomLhQ7VDOipKZzbkV6pTx4jKPqHJdV1B0BlZ1vIuxCUtaXfIEoAuKQGsMoSp5l2ObYrNQnbYfn459XHad4r8+w7frMHwiu4NiB/uJefcUuxXsdpddqzU7mltGcWXbWMqoNZAE6hSyE0vwgOpLiwgL6ECgXyiH00/MFG21WYjP2kPXiIa7Lrq07+t0DsDh9O0NnrNmx3/p22kkMe16Ylfs2JUTb7Q2uxW74n5vtNUVVrZoxpO26Bnsvq13HpeZKcEtfs3u/jF0zk9s8eu6gim0F9f5PsvyhCGnP1hFdnMV5pxEzDk1v2drSQ7mnESspbloNBoChs+hZNMXVB79C3NeMvmrX8LL2wdjnxPl8vzvn6do4weOx4rNcqJNuxVbeQHmnEQsRZl1rm/OS6Hy8G8En1OzTJIuNBo0XpTtWUtlwjYsBenoO/Ro3l9CK6Eo0gvlTqSEdwpSvnNmslSRV5LheFxQlk16fjx+PgGEBkQwecBFrN31Ke2DomkX1JGfdn2Kt86X4d3jHOd8tP4pgozhzBl1DQCTBlzES9/ezrrdKxjQeRz7Uv7kcMZOll/wcp3rZxUmszPhV+6dVzMXVERwJzQaDX8d/oFAQyg5xal0bt+rmX8LZ+5YlpH881+g36EP0B/cpHY4TdZxWyr0bdlrxmmDW/aCLnI0Zj7zk2dTYnH/t1hz9jFyVvyf43HR+ncAMPaPI/z8OwgcdTGK1UTh2jexVZfjE9WL9gsecZqjyVqaB5oT38dt5YVkfXCr43Hp1lWUbl2FT0x/Ihc/5diuKAqFP71GyJRr8dLXLJjs5e1D2MzbKVz3JorNQui0G9AFyLI2f9uWXMi5/VrhODoPpFEU6WNpyNcv7iTjSLHaYbiNo5m7eeW7O+tsH9XzXC6ffA+KovDDjo/489D3VJrKiG3fhwXn3EpU6IlJJl/6djlhARFcPvkex7ZdiRv5ftv75JdmER4YxewRdadKUBSFF7+5jWlDFjGg84lZkvelbOLzP17BarMwe8SVjO1zfjM8c9fSenvR35hI2NfPqh1Kk/3n1h6sMya12PW+qPand9bBFrve2VJ8gvhPyB08kdxT7VCEhxoUHcQ3N3vOXb6tmSRQDbDb7Pznjt+wmt2vJCQ8Q8cOGrqtfwpdTqraoTRa5pyR3N63ZWYE7+gXwZoD205/oJsobz+MJSXXsbMkQO1QhAfTeWk4+MgM9DoZgaM2+RdoQF5auSRPolllZClsG34/5RMvUTuURuu4reWSvTh9RItd62woGi+2xVzFsPQ7JHkSzc5qV0iWhYXdgiRQDchPK1M7BNEGVFVY2aqZQPrCp1F83P92fSUzm6mVLbPuX1x+xukPUpnNGMHjYU8y/9hUTHZ5OxUtIyFX7g53B/IX34CinIbXbxPC1Y5m+7N79suY+7j/XDfnJzf/PFthPiEMTt/T7Nc5GwUdJnJu1RO8kx6jdiiijYmXBMotSALVgBJJoEQLK8q3sCl6KQVz6g7UdyfRLVDGm+wXjZcbTkkBoGj1rIu+leHJ15FQ6f69hqdjqyol7dVLsZY0vGKAWvK+eoLSrV+pHYbbic+TBModuP89tiqRHihn5dUlPLbyKu666HXCAtzrFtp31j5El8h+xA2cr3YoZ81msbOnpCsdF79O93VPos1LVzukOpTMbOKquvOLIbnZrjG1uKDZ2j4blqAu3MVtfB3fXu1QXKZ00xcYuo1EF1Qz5izl6Vl1jgk99yYChsyss91SlEnWB7eBxotOt690bK888hdlu37AnJuIYrPgHd6J4HGLMXQd5jhGsVkp2fwFFft/wVpWgHdoR0ImXel0TNC4ReSs+D/8B013mjahrZMeKPcgCVQ9bDY7Zfmy6nVta3etYEDn0Y7k6ea34+occ8n42xnfd3ad7XklGTz15fV4abx49spvnfYdy9zDqk1vklWUTJBfOFMHX1JvGwDb49fzwS+PMzB2LNdNf9Sx/bxhl/Pyd3cytvdMDPrWO1FlbRmZUDjqAQaWrMP4+//UDqeO85NC+aVvcrO0HeDtz8j4Hac/sIWlRc/i4rT55Jq81Q7FZewWE+V719J+/kNO28Nm3o6hy4lERlNP8qLYrOR/+yw+0X0xZRx22ledth/fLoMJnrgELx8j5ft+JvfLR+mw5Hn0Ed0AKP79YyoObCBsxi3owmKoTtpJ3lePE3nZs45j9O27oAtqT8XBX+tN4NqqxLwKFEWpZxks0ZIkgapHaV4VdrvM7vA3s9XEpsM/cuN5Tzhtv2zSXfSNGel47FtP8mKzWXn/l8fpFjmApJwDTvvyS7N488f/Y2zvmVwx5T4Ss/ez8o9X8PcNYkjXCU7HFpbl8PXmt+kWOaDONTqGdSMsIJLtx35hfL8LzuapupWqcitbdJPptWg4UaseRmOqUjskh5jtac02qeYE/1i87e4z95PibeSzdrdyX3zd115rV524A7y0dZZl8fIxOtbBa0jx7x/jHRaNb+dBdRKo0KnXOT0OmXgFVce2UBm/1ZEcVRzYQNCYBRi6jQDAe8hMqpJ2Urr1K8Jn/8NxrqH7KCoObpQEqpYqi42M4iqiQ6RXTk0yBqoexVK+c3IwdStaLy1dI52XVzHo/Qn0C3X86HU+dc79btt7RATHMLTbxDr7/jj4HSH+7Zk3bhmRIZ0Z2+d8RveawS97Pnc6zm638cH6J5g5/ArCAzvUG+OAzmPZHr/+LJ6lm1LgSFYAe2a/hLn3CLWjcVAysphSGdssbU8td587YKvC+nOF97Pcl+h5yRPU9BTpI+suk1K47i3SXllM1od3ULbrB5STxqNVpeyh8vAfhE67sVHXURQ7dnMVXr7+J7ZZLaB1Xh9Qo9NTne6cPPt06Ikp62jN8cJBynjqkwSqHsU57vNN3x3EZ+2lU7u6Myt/8eer3PPhhTyz6iZ+P/hdnXXojmTsYlfibyw459Y65wIk5RykT/Qwp219Y0aQmn8Um83q2Pbjjo/x9w1ibO+Gv4F2bt+LlLzDWGyeudBmYb6VzTFXUTjnDrVDcZiVEuryNn21PoxLdo/y3YGYRQzPuYffCoPVDqXZWEtz0fo7/zsGjb+MdnPvJeKSxzD2GU/Rhncp2XTiS42tqpSC1S8Rdv4djR6XVLr1KxRLNcbeJ1YY8O0ylLJtX2MpzEBR7FQl7aLq2BZsFc5rvWkDwsBmwVZRdBbP1PNIAqU+KeHVozhHJimrrbA8myC/MKdts0ZcSc+oIeh1PhzJ2MlXm96iorqEGUNrFgQtry7hv78+wxWT72twXFJpVSEBBucyQYAhBLvdRnl1CUHGMBKy97PpyI/ce/G/TxljsDEcq81CWWURoQGtYwLGprJa7Owu6U704tfptvYJtCrPkxSzPR36nP64phgb2A2D+ZhrG20iuyGU1wPv4Plj3VSNoyUoFhMaf+e/7eCxCx3/r4/oCkDxn585theseRVj34n4xvRv1DUqDm6k5M9PaXfRA2iNwY7toVOvo2DNq2S+U9OLpQvpgHHAVCr2/ex0vkand8QqTkiQO/FUJwlUPeQOPGdmq5kgP+eu9r8TJYDo8O4A/Ljzv47tKza+wPDuU+geNfDUjZ80CFJBOb5ZQ7W5ko/WP8miCcvxNwSdshnv4+VDs9XzB/+nZ0LhmH8xsPAn/P5cpVocSnomk6u6s8GFd+PFVanbg1gaMYpLC69hX4pn3IxwOl5+gdirT/1BrI/qjWKuxFZRhNYYQnXKXqqObaF0a63XnmIn5ZkLCJtxM/4Dz3Vsrjj0GwU/vkL43HsxxA52alfrF0T7i/6JYjVjqypF6x9G8cYPHHcD/u3v+Lz8As/uyXqYhFz5oq82SaDqUZwrJbza/H2DqDSf+k02NqIv1eYKSisLCfQL5WjmLval/OUYz6RQMw7i1n9PY9GE5YzpfR6BhlDKKp2768urivHy0mL0CSSrKJmCsmzeXvNPx/6/l2689d/TeOCSD2kXFAVAZXXZ8ViDXfSs3VtlmZXN+jh6LRxRM8DcrE7iOCsllA29k13Slk6jY2Jyy6yzdzJFo2VT9NVcHj8Bm9J2Rjbo23ej4uCGUx5jzklAo9Pj5VMzfqnDZc+hKDbH/qpjWyjZ8j8iL3sWbUC4Y3vFwY0U/Pgy4bPvwq9bw+P3NDo9uoBwFJuVyiN/4dfbeaFcc14K2oBwtH6n/hLV1shcUOqTBOokpiorVaWeOY7mTEWHd2fbsZ9PeUx6fjzeWj2G42+yd8591WlM1N7kv/h592csn/sKwcaaN9kuEX3Zn7LJqZ1D6dvpFN4TrVZHRHAn/m/+O077v9/2HtXmKuaNW0aIfzvH9syiJIKN7U7bU+VRFDiSHUjenJfpu/cd9EdafuHdTtvTobdr2hoe1JWgxETXNNYENv8oHtYv56NjUS1+bbUZug6l+LcPsVWXo/X1pzJ+C7byInw69kaj86E6dS/Fv32M/6DpaHQ10zd4hzvPvG7OjgeNF/p2sY5tFQc3kr/6BULjrsMnqje28prxSxpvPV4+Nb17pswj2MoK8I7oiq0sn5I/PwXFTtCoi53aN6UfwDd2SDP+FlqnwgozhRVmQo360x8smoUkUCeRO/Dq6hM9nG+3vkOlqQw/nwD2Jf9FaVURXSL64q314VjmLr7b9h7j+pyP9/G7aiJDOju1kZp3BI1GQ1ToiXXUzuk7m98OfMOXf73BuD7nk5RzkE2Hf2Rp3P0AeOv0TsdDzZ1/QJ3tCVn76gxIbysK86xs7nQV/XuOI/S7l1r02kpaJpOquvGrIeWs25pqafmen9yoOOZlXUpqvm+LX9sd6NvFoo/sTuXh3wkYfB4aLx1lu36gaMO7oNjRBUUSPP5SAobWnVzzVMp2/wh2G4Xr3qRw3ZuO7cb+cYSfX3MjhGI1U/z7x1iKs/HSGzB0HUbY+XeedKeemcqjm4hY8IhrnrCHic8tZ2QX19/MIRpHEqiTSAJVV8ewrnQK78nOhF85p+9stF46fj/wLas2vYmiKIQFduD84Vcwod/cJrUbHtiBG897gi83vcHvB74lyBjGvHE315kD6nQsVjN7kv9k2cynmnSeJ7Fa7Oy29CBm8Wt0/elxtAVZLXbt2Slh/Nr77BIoDRqmpO51UUSnp2h9WN1hGTfHD2+xa7qr4LELKdrwHv6DpmPoOsxpJvDG8B8wFf8BU522RS4+/d+ib6cBRF3z5imPKd+7Fp+oXvh0dFE3p4eRBEpdGuXvQSUCgC3fJrL9h2S1w3A7B1K38NWmt/i/Be/ipXGvMSIb93/NvpS/uPn8Z9QOxS34BegYWPAjfn993SLX08R0ZP5lZ7eO2qDAbvx3z6nH4riKObg7t9tu5Ye88NMf3EaUbv8Gv55j0QW2O/3BLahs9xp8Y/rjHRatdihu6epzuvDArGaa0Vaclnt9EroB6YGqX79OoxjXdxYlFflqh1KH1kvH/HG3qB2G26gss7LZZxqZC59A0Td/aUpJy2BSVefTH3gKcUrdSVibQ2L0hYwueECSp5MEDp/jdskTQMDgGZI8nUJGkdzwpCZJoE5SnCsJVEMmD7iYEH/3W0T1nL6ziAiOOf2BbYkCh7OD2DvnZcw9hjb75WanhJ3+oFOYetJSIK6m+ATwfocHmBI/n0KL56xlJ9q2wgq54UlNkkCdpKJEXpDCcxTkWdnc5VqKZtU/G7yrdNp+5pN69vCPIaYg2XXBnKQyfBALNc/ycJKLZ/0UQmWFlfJ5pSZJoE5iqpT1loRnsZrt7CrvRfziV7GHRjbLNZS0DCaeYRlvqlfzTD2hoGFPp8sZlvUPthTLJIzC8xRJD5SqJIGqxVxtxW6VMfXCM6VmerHtnIepHDu3WdqfnXpmZby4nCQXRwJ2v3Ceb/c4c46eR5VN6/L2hXAHxVUW5D4w9UgCVYup0nr6g4RoxSpKrWzxmUbmJY9h17t24HbnMyjjxfhF0iv7kEvjKIocx0zzU7yWFuvSdoVwNza7QrFUTVQjCVQt1RXyQhSeT1HgcE4I++e+gqXbYNe1m5rB+OpOTTonTu+6mxIULx2/xtzE0JSbOFzu57J2hXBnMg5KPZJA1SIJlGhL8nOtbOp2PUWzbnZZmxekNG16gLj8dJdc1xoYw71Bz7D02Dkoiub0JwjhIWQclHokgarFVCElPNG21Aww70P84lexhUacdXuxOzIbfWw731AGpe0562tmdpzB+NJHWZnVPAPkhXBnBZJAqUYSqFqkB0q0VamZXmw/5xGqRjVtzbOTKSnpnFPduDm5Jhs6ouHMB8AqOgOrOt7F2IQlZFXLgqqibZIeKPVIAlWLTGEg2rKKUiub/c4j65JHsevOPCGZk9q4Ga3jis58VntTaC+u832W5QlDzrgNITyBjIFSjyRQtVRLCU+0cYoCh3JC2X/Rq5i7DTyjNhpTxgvUBzAiZecZtX80Zj4j8+5nXb4soiqE9ECpRxKoWkxSwhMCqBlgvqX7jRSfv6zJ5yrJ6Yw7TRlvojEWb3vT/t4UnyD+HfkQ5x67kBKLrslxCeGJZAyUeiSBqkXGQAlxgsVkZ2dFXxIWv4ItpGnTDZyujBdXVtqk9srbD+NinuGJ5J5NOk8ITyc9UOqRBKoWSaCEqCslU8uOCY9SNXJmo8/psiOrwX0GrS/jkrc3qh1F48W2mKsYln4HO0sCGn19IdqKQhm7qxpJoGqRmciFqF95iZXNxvPJvuSRRg0wV5LTGNtAGW9sQFd8LVWnbcNmjODxsCeZf2wqJru8VQlRH+mBUo+8K9UiPVBCNExR4GBOGAcuehVLl/6nPX5uA2W8uOrTv+EXdJjIuVVP8E5646ZEEKKtKpQESjWSQNUiPVBCnF5erpXNPZdRct4NpzyuvjKezkvHxOQdDZ6jaPWsjb6V4cnXkVBpOOtYhfB0lWb53FKLJFDHWc02bBa72mEI0SpYTHZ2VA0gYfHL2IPqX76lvjLeyMBuBFaV1N9mUBfu8H+G6+JHy3IsQjSS/cznohVnSRKo4+w2eRUK0VQpmTq2T3q8wQHmJ5fx4hqoNqRFz2Jc8UN8neO6xYWFaCusNvnyrwZJoI5TFEmghDgT5SVWNvufT/aCh1C0zvMzddmZ7fh/L40XU1L3Ou1XvI2siLqP8fGLyTV5t0i8Qngam3x+qUISqOPk9SfEmVPscDC3HfvnvYYltt+J7UmpjKmOBmBQYFfCy3Ic+6rC+nOF97PclzigxeMVwpPYpI6nCkmg/iavPyHOWl6OjS29b6FkxnWObXPTaspycfYT0x8ciFnE8Jx7+K0wuKVDFMLjSAKlDkmgjlMkgxLCJczVNnZUDyJx8cvYA8McZby49IPYDaG8GvEo5x+bTYVVq3KkQngGuwyBUoUsKPU3yZ+EcKnkTB0FU56kf/qXLKQdgd65zCm7hn0pRrVDE8KjWCWDUoUkUMfJGCjRvOxoNICXgkZDzf8DGk3NYzQKGkDjpYAGNBz/b63tCiceo/m7nePbOd4OSk0b1Byn1D7++H/h7/ZrjqtR63xqtit/x8GJ9mq2//3/dse5J37qbk/q24uh/kO4JnooejQMc9WvVAgBgE1qSaqQBOq45rsL7+QPTgUNmpr/etWUDjWakz4YveDvDyxXfnBqONEW1PMhevxYpdaH6Nl8cDp+FOfjFOzH/9f5OMe/gWKvdZxzG8pJ/0VRap6VUnub3fk4p2PsTsc69isn9tdur/bxiv1E23X2KXYU+9/t2B3HK0rb/mYYFhbD1B5L2GrZyvn+H/CK9QoyzG37dyKEq3nppByuBkmgHKrR61ac9KF4/ENWPjiFaLIhA2bQyzaUcrOJvYeLmBqzi0etm/gs8DnWlvqpHZ4QHkMr886qQhKo47y0XpTmNbyCvBCicbRaPeeNuQFjpgFFsZEWXQZpYDYPxEf5jitKLmdQ0F28Xj6GSrl7SIizptNIBqUGqZwep9VKF6gQZysysjvzht6FMcPgqOwmVWcCkJgY4jhucMmzPOP9JP2lI0qIs+Yl+ZMqJIE6zksnsyALcTZGDZ7LpOD5kH9icdNqfzsZx3t209MUfHw6O/aFVG/j7spLuSwoE3n/F+LMaeUvSBWSQB0nPVBCnBm93o855ywntqQXisl57F9GRLnTY7PZedZxrVLNecW38Ljxf7T3lrcjIc6ElPDUIe9Yx2m8vNBo5NchRFN0iu7PRQOW45tRfw9uki3H+XFSWL3HdS5fwRP225gU0MBqw0KIBskgcnVIxlCLVidj6oVorPEjFjHGbzZKoaXe/WZfhdTcdKdtaal2fPSd6j3eYE3l2tJF3Ba0G18Z1CFEo2gAjfRAqUISqFpkLg0hTs/PL5CLxt1NVH4nsDQ8XUd2VCX2emZItlgGnrL9kcWP8rT+RXoa5ENBiNPx18rHuFrkN1+LVgaSC3FK3boM54Ket+KdefrkJpncercnJoWe9tzwqt+5v2oJ84PymxyjEG1JqLdUTtQiCVQtvv4BaocghNuaMnopw7VTUUrqL9nVZvNWSMpLq3dfWqqCjz7mtG3olHLmFl/PwwGrCdXJW5UQ9ZEESj3yrlSLX1CQ2iEI4XYCAtsxb+y9tMuJAFvjJr7MiTJhsTScaFksgxp9/e6l7/EUdzHG39boc4RoKySBUo8kULX4BQarHYIQbqVPz/GcH3sd2qymzRie4p13yv3Jyacv49VmtMSzrOwSbgw6jLcMmBXCIVQvY3fVIglULdIDJUQNjcaL6WOvZ6BtLEqZ9fQn1GL3UkjITz3lMSkpCnp9dNNiQuGc4vt52vdNYn3lrUsIkB4oNcm7UC0G6YESgtDQjswffS/BWcFwBmti50eZqa6uPu1xVmvjy3i1RVSu4yHTVVwQWHJG5wvhScIkgVKNJFC1SA+UaOsG9ZvGuR2Wosk+8/FGqYbCRh2Xklz/pJqN4W0v4pKSq7g/YANBchu3aMMkgVKPvPPUYgwKVjsEIVSh1eqYOe5melcNRalsWsmuNkWjkFB46vLd35KTFfT6jmd8LYC+pa/xlPYBhhqbNkZLCE8R6i1joNQiCVQtMohctEUR7bsxb9g9BGQa4SzzkOIIK2UV5ac/8DibdfDZXRAINO1necVCrg5KRj5KRFsjY6DUIwlULQYp4Yk2ZsSg2UwOuwTyzrzXqbaUgKImHZ98FmW82jSKlSnFd/Kk30d01Mvbmmg7JIFSj7zT1OInJTzRRui9DVxwzh10Le2LUu26+ZUSS9NPf1Atrijj1dax4hses17P9MBKl7UphDuTBEo9kkDV4mv0x0srL0bh2Tp27MNFg+7EkKF3abul4TYKS5rWAwVgO8O78Rqit+WypORy7grcjFGWqRcezAsIkTFQqpEEqhaNRoMhMFDtMIRoNuOGLWC8cS5KwemXY2mq9JAzm1YgJSXcxZHUGFzyLE9rn6C/X7M0L4Tqgr21eMnEsqqRBOokfoEyDkp4Hl9DIBeecxfRhV1QLGcwuVMjJFRmnNF5SUmuLePVFmLazt2Vl3J5UCbyMSM8jZTv1CUJ1ElkHJTwNF1ihzC3963oM5rvz70yyEZOQe4Zn2+zubaMV5tWqWZG8S08bvyCCBlgLjyIJFDqkneTk0gCJTzJpJFLGOk9HaXY9SW72tLbNX7qgvqkJDdPGa+2zuWf8bjtNiYFmJr9WkK0BJlEU12SQJ1ESnjCEwT4hzFv3D1E5HUAa/NPMplkyTq785MU9PooF0XTMIM1lWtLF3N70G58vaSo1xB7ZQVlrz1L3sLzyJkxmsKbr8By+MApz6n8eiX5Sy8iZ8Zo8pfMpWrtd077Tds3k79kDrmzx1Py1AMolhNJvb28jPwlc7DlnN3rqK2JNbj2RhDRNJJAncQY0rRV4oVwNz27j+H8bjegzWyZ65mMdtJzz/5izVnGO9mI4kd5Rv88vQySRNWn9LlHMO3YTNB9jxH27ufoh4+h6K4bsOXVX6at/OZzyt95Ff8rrifsvf/hv/QGyl5+CtNfGwFQ7HZKnvg/DLPnEfrKB1gOH6Bq9SrH+eX/eRnD7HloIzq0yPPzFD38fNUOoU2T/r+ThEY1bYX4tiQhr4BfDyeSUVRCabWJpeOG0b9jpGO/oiisPXCMLYmpVFosdAoN5qKh/YkMCjhlu3vTs1iz/ygF5ZWE+ftxXv9eDIg+0e7OlAxW7z2M2WZjZJcYZg/q49hXWFHJvzdu5fZp4/D19nb9k25NNBqmjb6a0NxwlFLXTIzZGJmRlSgZZ9/LlZrSjo4t+OcXVvUn/6fZw+qg5/m8pPlLiK2FYqrG9NsvBD/2IvpBwwDwX3oDpj83UPXtF/hfvazOOdXrVmOYdTG+k6cDoIuKxnJwHxWffYDP2IkoJcUoxUX4zVmARu+Dz9iJWFMSATDv343lyEECbr2v5Z6kh+ju56N2CG2a9ECdJDymk9ohuC2z1UZUcCAXDu1X7/4NhxP57WgSFw7tx21TzyHQ14d/b9xCtaXhD/Pk/CL+u2kXwzp35M5zxzOsc0c+3rSTlIKa+YQqTGY+376X2YP6cO2EkWxPTudgZo7j/C937Of8gb3afPIUHBzJ/NH3EZodBvaWXRcuyZ5z+oMaITFRQa9v2R4InVLOnOLredj/e0J18nYIoNhsYLeB3rk8pPHxwbx/V/3nWCxoTjoeHx8sh/ejWC1ogkPwCgvHtG0Tiqkay96d6Lr2QLFYKHvpCQLvuB+NVuYzaqoeRumBUpO8Y5wksF0EOh/J6uvTp0N7zhvQiwHRdT/kFEXh92NJxPXpzoDoDnQICmDhyEGYbTZ2pTZ8e/vvx5LoERFOXJ/utA/0J65Pd3pEhPP70WQACsorMXh7M7hTFJ1Cg+nePoyc0poByztTMtB5edUbT1syoM8UZkRfjVe262YUbyyLj0JKbprL2rPbBrusraboXvY+T3EXY/1brufOXXn5GfHuO5CKj/+DLT8XxWajat1qLIf2Yy/Ir/cc/YgxVP3wNZajB1EUBcuRA1Sv+QasVuwlxWg0GoL+9QwV//0P+VdejK5HbwznzaFixXvoh4xEo/el8Jal5C+ZS+VXn7XwM26dwrx1hMggclVJAnUSjUZDWMcYtcNodQorqiirNtEr8kQpRKfV0q1dGMn5Dc9OnVJQRK8I5/JJr4hwko/3QIUHGDFbbWQUlVBpMpNWWExUcCCVJjM/HTjaYG9YW+DlpWXmuGX0NY1AqVDngz+7QxU2m+sSt5SUdi5rq6mMlnhuKlvITUGH0LfxyQkD73sMFIX8BdPJnT6KylUr8I07D7T1f2T4X34t+pHjKFx2BbnTRlD8zzvwnX4BABqvmp4l/YAhhL35Ce0+XU3gbfdhy8qket1qjFfdRMmT/8Qw+2JCX36P8o//jSXhaIs919aqh5TvVCfpaz3COsaQkxivdhitSll1NQD+vs5/1P6+eooqqk5xnqmec3woq6651dxP783CkYNYsXUPFpuNYZ2j6RXZjpVb93BO91gKKip574/t2Ox2zu3Xk0ExbaM3ql14LFO6XQqZ6vaYJGvzXNpeYqJCl66RmM3ZLm23sTQojCv+Jz38pvKKsoyk6uaZdNTd6TrGEPrSuyhVVdgry9GGtaP4kXvQRtY/4anGx5egux8icPn92IsK8QoNp+r7L9H4GdHUMzWMoiiUvvAo/jcsB7sda/xhfCdMReNrQD9wGJY9O/Du1rOZn2Xr1l0GkKtOEqh6hMV0VjuEVqvO93alplfvlOfUs7v2pgHRkU6DyuNzC8gqKePCof156ocNXDp6CAG+Przyy590bRdKgK9nfzMbOnAmPa2DUXLVTZ5sOoWkvFSXt2u3DQF+dHm7TdG+8mce9NrBV0HP801J253aRGMwoDUYsJeVYt72F/7X337q43XeaNtFAFC94Sd8Ro9H41W316rqh6/wCgzCd9wk7GWlAChWa83fvc2KYm+biWtTyABy9UkJrx5h0VLCa6oA35pvQ3/3HP2t3GTG36fhuUoCfH0oqzrpnHp6pf5mtdlYtXM/84YPIL+8Apui0K19GO0D/Qn3N5JaWHx2T8SN6XR6Zp9zGz3KBqBUtfx4p5PlRZkxm80ubzc1Vb0yXm3e9iIWFF/FPwN/IaiB0pWnMm37C9PWP7FlZWDavpmi5deijYnFMKOmLFf2n1coefKfjuOtaSlUrVuNNT0Fy6H9FD96D9bkBPyvuaVO2/aiQir++w4BN98DgFdAINrOXaj88lPMB/Zg3rkVfb+Wm9KitZIB5OqTHqh6hEVLD1RThRoNBPj6cDQnn44hNd/YrTY7CXkFnD+wd4PndQ4L4WhOPhN6dXVsO5KTT2xYSL3HrzsYT+/IdkSHBJFRVIJdOXHHmV1RUJSWvQOtpUR16Mn46PmQ4T6DnFP09Q8oPlsJCXZiYyMxW9Qp452sT8kbPK3fyH98H2ZHRdsYG6VUlFP+n1ex5efgFRCEz/g4/K9ehkZXc7ervTAfW26tfx+7jcovPsaaloJGp0M/eDihr3yANrLu5Kilrz2DccEStO3aO7YF3f0IJU//i8qvVuB3yRK8+/Rv9ufY2kkPlPokgapHUPuaO/GsJlnyoTaTxUp+eYXjcWF5JRlFJfjp9YQYDYzv0YVfDsUT7m8kPMDI+kPx6LVahnQ6MW5ixZbdBBl8mXk8qRrfI5Y3Nmxm/aEE+neMYH9GDsdy8lk2ZUyd62eXlLEnLZM7zh0PQPsAfzTAlsRUAnx9yC0tJyYkuFl/B2oYPeQiYit7oeS7T/Jk91JIKHB9+a6GBrt9MLCmmdpvugDzAe6wLGRD0FN8UNIF9fv/mpfvpHPxnXRug/uD7nnE6bGuc1fC/t24u+eCH3iqzjbvPv0J/2BVPUeL+hi8NMT4yizkapMEqh4ajYbQqGhykxLUDsWtpBWV8Navmx2Pv91zCIDhsdEsHDmIyb27YjleYqsyW+gUFsy1E0fhW+tW26LKKqcxUbHhoVw6eghr9h/hpwNHCDP6cfmYIXQ+qQdKURT+t30fFwzui4+upj1vnZaFIwexaucBbHY7Fw7tR5AHDaz08TVy3vAb8MnQoeBeY0IKIi1UFlY2W/upqe2Iqn+8smo0ipUpxf+gt3E2L9uuIt3kXv8mou3oYvDBq43fKeoONIqn1jzO0o+vPc/B3zeoHYZoozrHDGRM+wtQCpt3EeAztbNHDjvT9jfjFRSmxK3DYnHNJJ2uZta2Z6X/c6wpNaodimiD5rQP5u1+sWqH0ea1rZGRTRAaLTOSC3VMGLGY0Ybz3TZ5Aogvbq7y3d80KMqQZr7GmdPbcrm8ZAl3B/6FUSs9AaJlyfgn9yAJVANkSRfR0ozGEC4edw8d8mPA4r7loeIIK6XHbz1vTmlucjfeqQwqeZ5ntI8zwE/tSERbIosIuwdJoBoQ1lESKNFyenQdyeweN6HLVDuS00sLLG6R68THK3h7tz/9gSoLNu3grspLWRKUIW+ookVID5R7kL/3BgS1j0CnlxepaGYaDXGjr2Ko1xSUEve5y+5UEsrTW+hK7l3Gq02rVDO9+FYeM64kQt+63lbtJcXkXjQFW7b7Ze/FD/2Dii8+VjsMt6LXaGQWcjfRuv7SW5DGy4vQjtFqh+FWKkxmHvxmHYUVzXf31Zn68K8dbDySqHYYTRIU2J75Y+4lPKcd2FrHvRzloTbyiwpa7HrpaREtdi1X6Fz+OU/YbmFyQOuZAqXi0/fwGTPBMWdTzpQhdX4qv/3Ccbw1NZnC5deSd3EcOdNHkX/pLMrffR3F6jxmTzGbKX/3NfIWnkfO9JHkXzqbqh+/PtFOUgLFD95J3qKZ5EwZQsX/PqkTm/Hy66j45F3sFeXN8+RboUEBfvi2sYld3ZVMY3AKEV26yVQGtaw/FE+/qPaEGmsGfPzj89V1jrloaH/Gdq+ZiDS3tJwvd+wjp7ScaouVQIMPQzp15Nx+PdDWWt7hz2PJ/BmfTGFlFSF+BuL6dGd4rHPyWmW28OO+I+zLyKbKbCHUaGD24L706VBT4pnWtwdv/bqZUV1j8PX2bq5fgcv07TWBgfrxKFmto9fpb2lhpdCC+fOxY3ZiOrXDYnHtmnvNydeazjWlixkcfD9vlg2j2u6+ybFiqqbqx68JfvJVp+2Bdz+MfuRYx2Mvo/+JnTodhmmz0PXsjZcxAEvCUcpeeBRFsRNQa+bxkkfuxl5USOBdD6Lr2Al7USGK7cTrXTFVo+0Qje/EaZS98Xy98Xl364k2Iorqn3/Ab84CFz3r1m1UsNz56S4kgTqF6L4D2Ld+rdphuAWL1cbWpDSuHj/SafslIwbSK/LEYF9DreRF6+XFsNhookOC8PXWkVVcxhfb96IoimMizb/iU/hh3xHmDx9ATGgwqYXF/G/7Xgx6b/pF1fQ+WG123t64BX9fPUvGDiXI4EtJZTU+teaXigoOJMRoYGdKpiOBc0cajRfTx1xHUHYQirl1JU8ASdUtXebRgDIU+KmFr3v2hhc/zrOGcbyuuZPDVe6ZRJm2/AlabZ2lUzT+AWhDw+s9RxcVjS7qxBccbWQUlj3bsezbdaLdrX9i3rOD8E++xyswyHFcbd69++Hdux9QszRMQ3zGTqR6/RpJoI4bFSQJlLuQBOoUomU5AYfD2bl4eXkRG+48waVB702gof56fJi/H2H+J25PCjX6kZDXkaT8Qse2HSnpjO7WicGdohznpBYUseFwgiOB2pqURpXZwi1xYx09V3/3gtXWLyqCXanum0CFhUYT13MJmqzWOY91dYCdzLyWX14lLa09kR1a/LIuEVr1J/+n2cXqoBdYWeJ+dxVa9u7Eu1ffOtvLXnmK0uceQdshCsN5czHMurjeRYEBrBmpmLb9he85cY5tpr824t2rLxWffUD1utVoDAZ8xkzE/6qb0Pg0bfyOd+9+VHz6HorZjEbftmff9gJGSgLlNiSBOoXA8HYEtY+gJNc9J/NrSYl5hUSH1F2V/qudB/h8215CjX6M7BLD6G6dGpwhN7+sgsPZeQzoGOnYZrXb8T7pjdlbqyWtsBib3Y7Wy4uDmTl0Dgtm1c79HMjIweijZ2injkzu3Q0vrxPXigkNZv2hBKw2Gzqt1kXP3DUG959Ob/swlJzWmTwBpEeUQ0uNH68lPp5WV8arTatUckHxDfQLuIKXTXMpcKMpKmw5mXiFOSd2xitvQj90JP/f3p3HRVWvDxz/zAYDDAz7jiAgAiq44L7nvqXm0qZllq3ar9ttsW5WttyblnXrtpnZ1ZabdVusbM+ycsnSNE0Rc0EQUfZ9G2bO7w+uo4SoKHCG4Xm/Xr5eMnPO9zwDzJyH7/J8Na5Gan7dSunLT2MrLsI0e1694wrmX4vlj31gqcFt4jQ8rrvlVLvZWdTs3gkurng/8jS24kJKnv0HttISzPc83KQYtf6BYKnBVpB3xr312pN4DyNmg9y2HYX8JM4hPLGbJFBAQXklZrf6qxLHdo0jNtAfg07LHzn5fPJbKhU1NYxM7FTvuH+t30RWYQm1Nhv9ojswpmuc/bnOQQFsPZxJ17Bgwny8OFpYzM+HM7HaFMqra/ByM5JfXsGBnEp6RoZyw+A+5JaV8+Gvv2NVFEZ3OXUts5uRWpuNkqrqM/ZQqUGnc2Fcv5vwyHZHUdpu8gRwuDZblesqCqD0ANr2cHpM6Wr+YdjIKtPf2VzmGB+9SnU12j+tNj49UTLEdgag/I0VDRIo84NLUCrKsRzcT9nyf6J793U8rphT96TNBhoN5vsfR2vyBMCzpobixXfj9X8Lm9QLpXF1/V+sVU19eU6nr7fp3AeJVuMY72IHFpHYjT0bvlE7DNVZrFb02vofeqcnSmH/6536Zu8fDRKo2f17UmWpJbu4hHW/7eP7tEMMj48B6iZ/l1ZV89z6TQCYjC6kRIWzIe2Qfc88Ral7fHqvJLRaDeG+Zkoqq9iQdqheAmX4X6+TxeoYiUpwUAxDI6+AY21vrtOf1bjZyMxRb5n70aNBBAWf+zhH52E5yK2WK+jh/SgrShKpUXknLa3ZG+UcRVENiUko5WVYC/LR+frZH9cF1v1A9FExYLNR8vRjuM+YjUanQ+vnj84/0J48AegjO4KiYM09gT78/IfZT8an9fY5x5HOT+Y/ORZJoM4hIrGb2iE4BA9XFyotZ99aJNLPmypLLaVV1XgaT/1V6+3uBkCw2RPb/zYFHhoXjVarwaDXcXmfZKandKO0qhovo5GfDmXgqtfj4Vo338HLzRWdRlNvuC7Qy0RpVTW1Vhv6/y3praipAcDkqn79rj7dpxBdlYiS2/aTJ4DskApsx9QbevrjDwiP8MdiyVMthuaiQWFA0QPEuo3gX8znUJV631d9bDxV3zRcTXu62j/2gYtrvWSoAUWB2lqgLiE0dO1O1fffYKusQOtW1xtce/QIaLXoAppWmqL28AG0AUFozZJA9ZMeKIcixSTOwSsgEK8Ax6+G3NLCvL04UXL2WixZhSXodVrczjZGr4DVpqBQ/y9vnVaLt7sbWq2GnZnHSAwNtM+livLzIa+sAttpf63nlZbjZXS1J08Ax4tLMbsZ7YmXGlxc3Jk86E46FndGqXaMnrDmkE6Oqtev+9H3VDWG5hZYuZ4Hq+cy2VykWgwuvftTm34I2/96eao3f0/Fug+oPXyA2qxMKj79gLLXXsBt4mX2CdyV33xG1YavqD1yiNpjR6na8DVlr/4L4/DRaHR1733jiHFovcyULHmI2vSD1Py2nbLl/8Rt7GT78J1isWA5kIblQBrUWrDl5WA5kEZtVv19Fmt278AlpV8rflccU6TRhWBXxy/R0p5ID9R5iEjsxp7v16sdhqo6Bwfw2e40KmosuLsY2HPsBKWV1UT6+2DQaTmYk88Xv6fRL7qDfQL3r0ey0Go1hJg90Wt1HC0s5rPdaXSPCLGvpsstLSOjoIgOvj5U1lj4Yf8hjheXckWfU8uqB8RGsulAOh/t2MOgTlHklpazPvUAgzpF1YvxcF4hnYPPvPS6NUSEdWFg8FSULMfdBPhC1LooHM7JVDsMjmY6xzDe6Qy2QmYWXU+S1y08Vzma4trW7Y0yRHfC0DmBqg1f4T5pOuj1VH78LmUvLUNRbOhDwjHNuQW3KadKCGh0OsrfXoX16BFQFLRBIbhPmYn79Fn2Y7Ru7vg8+RKl/1pC/i2z0HqZMQ4bhWnubfZjbPm5FNx4hf3rindfp+Ld1zEk98L3mVcBUGqqqd74Hd5LXmiF74Zjk/pPjkejKCoPwrcBv3/3NV++/KzaYajuX+s3kRIVTv+YSPZl5/DZ7jTyy8qxKXXlB/p2jGBAbKQ9OdqZcYzv0g6SV1qOAvi4u9EzMowhcR3t85VOlJTy1k87yS0tQ6fVEhPgx4SkeAK96ndVp+cV8vHOvRwrKsHsZqRPx4h6q/AsViuLP/6GeUP6EOnX+l39g1KuIKyko0NvAnyhsqIq+fz4ZrXDQKOB4Zd87hTDeGdS6pLAqy6PsK28dQcGqn/6kdLlz+C38r1GSxWopWLtO1Rv2oDPky+pHYrqnu4cwVWhfuc+ULQaSaDOQ3HOcV5dcIPaYaguNTuHT35L5a4xQxotVaCWTX+ks+fYCW4c2rdVr+vu7sXYHjdhOOZYN57mtDk2g71H/1A7DAAuGXEci+VrtcNoMTZ0fO/9BKtKoqltxU/mivf/g+vgS+wTwx1Fxbr3cUnqhb5DlNqhqG5j33jZA8/ByBDeeTAHBuPpH0BpXtusQ9NcEkICySstp6Syyj4x3FHotFqm9OjSqteM7tiLPt7jUI4515Dd6Ww6hUO56g/fnZR1NJjAtrU9XpNosTK86G46e0zkOev1ZFa3To+m+7SrWuU6TeU+cZraITgEf4NekicH5Lx/NjezCKlKDsDguI4OlzwB9Ivp0GDYryVd0vdaeutGoRQ5b/IEkBtSTZUD1d/Zvx8MBucfxggtX8fimnmM9SpXOxThAGT+k2OSBOo8hUs5AwF4evozfcBCAnKCwer8o98ZbgXnPqgVOeNqvMa42vKYXXwN93ptwqRzrCFz0bqk/pNjkgTqPIUnSg9Ue5cQN4gJHW9Cl+38iROAolE4WJBx7gNbWdZRx5qn09KSip9mqe5RkhyjuL5QgdR/ckySQJ0nn+BQTL7OP3QgGtJotIzufyNJ1oEopc5RGPN8FATXUlbueENI+/cr6PW+aofRqszVO7ir4mquMWfJh3Y7E+xioJvJ8aZNCEmgmiSiS5LaIYhW5usTyox+C/E57gPOV6HgrDJMhWqHcEaKokGjaR/DeKfTKVWMKbqdxz3WEOwiH93txWh/L/u2VsKxyLuwCWJ6te4SeaGupMSRjA69Ds1x56ko3hQHix1n9d2fHcsKUTsE1XQo+y+P187nEs9qtUMRrWCsv1ntEEQjJIFqgugeKegN6m0TIlqHTqdn/MD5JFT1QqloP0N2pysJqKWopEjtMBqVlqag17ffvdGM1iyuL7mKv3j9ilErvRPOyqTTMshH5j85KkmgmsBgNBKZ3EPtMEQLCgzsyPSUe/E85gHtY674GWV4F6sdwlkpigZtOxzG+7OU4sdZaniKeDdJopzRcF8vXBysOrw4RX4yTRTbu7/aIYgWkpI0kUv8roSc9tnrdLpD5Vlqh3BOx46Fqh2CQ/Cr2sz9lbO4wqzuhs+i+Y3191I7BHEWkkA1UUxKX7T/28dNOAeDwcilg+4gprQLSlX7nO90unJvGzkFjl91f9++9j2MdzqdUsGkolt4xPQx/gb5WHcGeg2M8JMEypHJO62J3EyehCe07pYhouWEhcYzLfku3LJc1Q7FYRwNKFE7hPMiw3gNxZSu5h/KnQw0SS9qW9fXbMLbILutOTJJoC5AbJ8BaocgmsGAXjMY7DkVJd+5t2NpqsPV2WqHcN6OHWu/q/Ea4245zC2lVzDfvBcXWf7eZk0K9FY7hLN6+OGH6d69e6PPr1q1Cm9v74u6xpw5c5gyZcpFtdGSJIG6AJ169wf5YGqzjEYTUwfdRURBNEpNOyvudA5VHjaycttOApWWpkGv91Y7DIejQaF/0SKWuD5PjFE+5tsanQYmBLRs+YLNmzej0+kYO3Zsi16nLRg2bBh33HFHk89rM++spmai6enpaDQadu7c2eyxmHz9CI+XYby2qGNkd6Yk3oFLlsxjO5NjweUoSttZfmizIcN4ZxFY+S2LquYw1eyYRVHFmQ3wNhHgYmjRa7z22mssWLCAjRs3kpHheFs2tQVNTqBycnK46aab6NChA66urgQHBzNmzBi2bNnSEvE5rPiBQ9QOQTTR0D6z6eMyFqVQhuwac9h2Qu0Qmiw7W1bjnY1BKWZ60Q0s8vwas77N/M3crl3awsN35eXlvPvuu9xyyy1MnDiRVatW1Xt+w4YNaDQa1q9fT0pKCu7u7gwYMIC0tLRG2zx8+DCxsbHccsst2Gxn7tn/5JNP6NWrF0ajkejoaBYvXkxt7bnn6y1evJjAwEC8vLy46aabqKmpsT9XXV3N7bffTmBgIEajkUGDBvHLL7/UO//777+nT58+uLq6EhISwsKFC+3XnTNnDt9//z3PPvssGo0GjUZDenr6OWOCC0igpk2bxm+//cbq1avZv38/H3/8McOGDaOgwLF2bW9pnfoOlNV4bYTJ5MP0gfcSnBsKtW2nd6W1WYw2jpxw3Orjjdm3T4bxzkd8ycss1dxHbw8ZtnZkeg2M9/du0Wu88847dO7cmc6dOzNr1iz+/e9/n7Hn+W9/+xvLli1j27Zt6PV65s6de8b2fv/9dwYOHMiMGTN46aWX0J6hdtWXX37JrFmzuP3229m7dy/Lly9n1apVPP7442eNdf369aSmpvLdd9/x9ttv8+GHH7J48WL78/fccw/vv/8+q1ev5tdffyU2NpYxY8bYc5KsrCzGjx9P7969+e2333jppZdYuXIljz32GADPPvss/fv3Z968eWRnZ5OdnU1ERMR5fR+blEAVFRWxceNGlixZwvDhw4mMjKRPnz7cd999TJgwAYCnn36abt264eHhQUREBLfeeitlZWX2Nk5OLPvyyy9JSEjAZDIxduxYsrNPzbuwWq3ceeedeHt74+fnxz333NPgh/vFF18waNAg+zETJ07k4MGDTXk5F8Xdy0xkt+6tdj1xYeJi+zEx5lZ0x9SOxPFlh1Q2+pejI7PZQKuVYbzzYarZx+1lV3Cj+QB6mcbpkAZ6e+Ln0rKr71auXMmsWbMAGDt2LGVlZaxfv77BcY8//jhDhw4lMTGRhQsXsnnzZqqqquods2XLFoYOHcqdd97JP/7xj0av+fjjj7Nw4UKuvfZaoqOjGTVqFI8++ijLly8/a6wuLi689tprdOnShQkTJvDII4/w3HPPYbPZKC8v56WXXuLJJ59k3LhxJCYmsmLFCtzc3Fi5ciUAL774IhERETz//PPEx8czZcoUFi9ezLJly7DZbJjNZlxcXHB3dyc4OJjg4GB059k50qQEymQyYTKZWLt2LdXVZ96HSavV8txzz/H777+zevVqvv32W+655556x1RUVPDUU0/xxhtv8MMPP5CRkcFdd91lf37ZsmW89tprrFy5ko0bN1JQUMCHH35Yr43y8nLuvPNOfvnlF9avX49Wq2Xq1KmtegOIHzi01a4lmkijYWT/G+ihDEMpkSXd5yNd6/i1nxqTLUU1z5sWK0OL7uUfbv8mwlWG9BzN5BYevktLS+Pnn3/miiuuAECv13P55Zfz2muvNTg2KSnJ/v+QkLoVrzk5pwq2ZmRkMHLkSB544IF69/Az2b59O4888og9jzCZTPZen4qKikbPS05Oxt3d3f51//79KSsrIzMzk4MHD2KxWBg4cKD9eYPBQJ8+fUhNTQUgNTWV/v3719uQeeDAgZSVlXH06NGzxnwuTUpz9Xo9q1atYt68ebz88sv07NmToUOHcsUVV9i/0afPZO/YsSOPPvoot9xyCy+++KL9cYvFwssvv0xMTAwA8+fP55FHHrE//89//pP77ruPadOmAfDyyy/z5Zdf1ovl5HMnrVy5ksDAQPbu3UvXrl2b8rIuWGzvfuhdXKmtkU09HYm3dzCjEq5De9xGu96PpQlq9QqHc9ruRNJ9+zQMHWamttaxt6BxJKHl61is/Yn3vJbxWYnst+YIXDQaxrXw6ruVK1dSW1tLWFiY/TFFUTAYDBQWFuLjc6o4rcFwaiL7yQTk9E6KgIAAQkNDWbNmDddffz1eXo0X/rTZbCxevJjLLruswXNGo7HJr0Oj0dhHpjR/WhWvKIr9sdP/f/rzZzqvqS5oDtSxY8f4+OOPGTNmDBs2bKBnz572SWjfffcdo0aNIiwsDE9PT6655hry8/MpLy+3t+Hu7m5PnqAusz2Z1RYXF5OdnU3//qe2TNHr9aSkpNSL4+DBg1x11VVER0fj5eVFx44dAVp1NYGLmzvRPVLOfaBoNV0ThjE24vr/JU/ifOWGVmOxtN3J9TKMd2FcbXlcXXwtC71+xKSTMT21jQ8w49OCxTNra2t5/fXXWbZsGTt37rT/++2334iMjOStt95qUntubm6sW7cOo9HImDFjKC0tbfTYnj17kpaWRmxsbIN/Z5ozddJvv/1GZWWl/euffvoJk8lEeHg4sbGxuLi4sHHjRvvzFouFbdu2kZCQAEBiYiKbN2+uNw1o8+bNeHp62pNIFxcXrNam70JxQf23RqORUaNG8eCDD7J582bmzJnDQw89xJEjRxg/fjxdu3bl/fffZ/v27bzwwgv2F3XS6Vkt1M8kz9ekSZPIz89nxYoVbN26la1btwLUm53fGuIHyTCeI9BqdYwbcAtdqvuilMmQXVMdcclTO4SLdjw77NwHiTPqVvxPluoeJdn93MeKljMnzL9F21+3bh2FhYVcf/31dO3atd6/6dOn2+cNNYWHhweffvoper2ecePG1ZvzfLoHH3yQ119/nYcffpg9e/aQmprKO++8wwMPPHDW9mtqarj++uvZu3cvn3/+OQ899BDz589Hq9Xi4eHBLbfcwt13380XX3zB3r17mTdvHhUVFVx//fUA3HrrrWRmZrJgwQL27dvHRx99xEMPPcSdd95pT9yioqLYunUr6enp5OXlnfdUoGYZAE9MTKS8vJxt27ZRW1vLsmXL6NevH3FxcRw71rTZu2azmZCQEH766Sf7Y7W1tWzfvt3+dX5+PqmpqTzwwAOMGDGChIQECgvVqXMS06svJl8/Va4t6vj7d2BG74V4ZXvJiN0FsGkVDua33eG7k/bt06DXyd5hF8pcvYO7Kq7kWnNm2ykQ6ETiPYz0827ZodSVK1cycuRIzOaGw4TTpk1j586d/Prrr01u12Qy8fnnn6MoCuPHj6834nTSmDFjWLduHV9//TW9e/emX79+PP3000RGRp617REjRtCpUyeGDBnCzJkzmTRpEg8//LD9+SeeeIJp06Yxe/ZsevbsyYEDB/jyyy/tQ5FhYWF89tln/PzzzyQnJ3PzzTdz/fXX10vc7rrrLnQ6HYmJiQQEBJz3SJZGaULXT35+PjNmzGDu3LkkJSXh6enJtm3bWLBgARMmTGDBggX06NGDf/7zn0yaNIlNmzZx3333kZWVRWFhId7e3qxatYo77riDoqIie7tr165l6tSp9l6oJUuWsGTJElauXElCQgJPP/00a9as4ZJLLmHt2rXYbDYCAwMZN24cDz30EBkZGSxcuJBffvmFDz/8kClTppCenk7Hjh3ZsWPHWcvNN4ct77/N5neb1vUpmkePbuPobO2BUimbAF+o3PBqPsrbeO4D24ARI49SU/Od2mG0eZmm6TxruZpsqdTfav7eKYy54QFqhyGaoMmr8Pr27cszzzzDkCFD6Nq1K4sWLWLevHk8//zzdO/enaeffpolS5bQtWtX3nrrrbMua2zMX//6V6655hrmzJlD//798fT0ZOrUqaeC1mpZs2YN27dvp2vXrvzlL3/hySefbPJ1mkvSiLHo9LLpY2vS612YNOj/iCtLkuTpImW4OU+VahnGax4RZe/xWO18RnhVnftgcdE8dFpmBPuqHYZooib1QInGffavp0jduEHtMNqF0JA4hoTPRMlru5OeHYWiUXjX7xdKyxqf/NmW6HQwZMhH1FpL1A7FaWz3vo+XSntTaZNbRUu5JtSPpZ3Pr3ijcBwy1N1Muo+ZqHYI7UK/HpcxxGuaJE/NpDCw1mmSJwCrFbS6XmqH4VR6Ff2DJYaniHeTVXot5doWnjwuWoYkUM0kNC6eoOhOaofhtFyNHkwZ9FciizqhyLyMZpNpLlI7hGZ3/LgM4zU3v6rN3F85iyvMJ5A0qnmleLnTxeSmdhjiAkgC1Yx6jJVeqJYQGZHE1C5/wTVL5pk1t0MlF1eJ1xHtSwWdrMZrdjqlgklFt/KoaS0BBrl1NBfpfWq75F3QjDoPGIKbp3xwN6chva+in9sElAIZsmtupX615Bc53ybgVqsGvU6KaraUjqVv8LjtLwzylPfkxfI16Li0hbduES1HEqhmpDcY6HbJaLXDcAru7mamDbyXkLwIsMiQXUvI9HXeidYyjNeyPGrTubnkSuabf8f1IrfDaM+uCPbD9SxVuIVjk59cM0sePR6NvCEuSqfoPlwatwB902qwiiY6VOm83+B9+7TodJ5qh+HUNCj0L3qIJ1yfJ9YoSVRTaYBrwqQIc1smd/pm5uUfSEyvvmqH0TZpNIzodx09tZegFMvwQEuqMNs4nndC7TBaTG0t6GU1XqsIrPyWB6quY6rZeeqJtYZhvp5EubmqHYa4CJJAtQCZTN50Zq9AZvRfiP+JQLBKvZmWlhXgPKULGnP8hAzjtRaDUsz0oht40PMrvPVyWzkf14bK5PG2Tn7TW0CHrsn4hXdQO4w2I7HzEMZFzkObLXOdWsshS7baIbS4faladLqW3VtM1Ne5ZDlLNPfRxyS7A5xNmKuBUf6y4KitkwSqhUhhzXPTaLSMGXAT3Wr7o5TVqh1Ou1HjbuNojvPOfzpJhvHUYarZx4LSK7nRfAC9TI06o7nhAehk8n2bJwlUC+ky5BJc3T3UDsNh+fmGM6PvQryzvUE6nlpVVkgF7WUHpxM54a12rV27Knngb8e5fOYRRo44xKaN9XekVxSF1asLuHzmEcaPO8yddx4jPb3mnO3+8EMZc6/LZNzYQ8y9LpONf2p3/TelXHnFEaZOSWf58vx6zx0/buHaazIpL2/dN5kWK0OL7uUJt5V0cJXbzOn8DXquk9pPTkF+s1uIwWgkaeRYtcNwSN27jGZUyDVoTrRuN/9PmTu57r2F9HphKhFLhvDF/h/rPa8oCk9vfI1eL0wldtlIZvzndtJyD5+z3c/SNnDJq7OJeWoEl7w6m8/3/1Dv+Q/3fEWfF6fR9dkJPPbdi/WeyyzOZsgrV1FaXf+m2JLSFeedPP5nqXtbbxivqlIhOsaF+QvOfHN8Z00x779XzPwF/rzwYhi+PjruvSebiorGk5u9e6p47NEcRo4ysfyVcEaOMvHoIydITa3b5Le42MqyZXnceJMf/3gimK+/KuWnnyrs5z/7zzxumOeLh4c6H/Uh5Z+xuGYeE7zKVLm+I5rfIRB3ndx6nYH8FFtQ70un4eLmrnYYDkOnc2HCwAV0ruyBUtH6cyQqa6pICIzhsZF3nPH5l7b+hxW/vMtjI+9g3TWvEODhy1Xv3klZdcUZjwfYnvU7t360mMu6jOHL617jsi5juPWjh9hxbC8ABRVF3P3FUh4YfitvznyK937/gvUHt9jPv//Lp7lv6E14urZOb2Wti0L6icxWuZYjqK0Fvb51imr26evO3Lm+DB7c8GepKAoffFDMVVf5MHiwBx07unDPvYFUVSl8u77x5OL9D4rp1cuNq67yoUMHF666yocePd344P1iALKzLXh4aBk+3ER8vJHk7m4cOVLXq7V+fRl6g+aM8bQmF1seVxVfy31eP+LZzhOHQBe9VB53Iu37t7mFuXl60WvCZLXDcAjBQTFM73U3pmPuoNLo0fCYftwzZB7jOg9t8JyiKKzc9l8W9J/NuM5DiQ+I5pkJ91NlqWZt6teNtvnqtv8yOCqF+f1nEesXyfz+sxgY2YtXt/0XgCNFx/ByNXFpwgi6hyTQv0MP/shLB+DDvV9j0OnPGE9LOR5WidXavib4njih/i732dm1FBRY6ZVyas8zFxcNSclG9uypavS8vXur6p0DkJLixp491QCEhRmorrbxxx/VlJRYSUurJjrahZISK6tXFbCgkd4wNXQt/idLdI/Q3aN9DB+fyfwOgbi18yTSmchPsoX1mjAVo6l9F/Tr030yw3wuh1zHnSieUZxNTnkBQzr2tj/mqnehb0Qy27N+b/S8X7P21DsHYGjHPvZzOvpGUGmp4vcT+ymsLGFX9j7iA2IorCxh2Y+v8dioO1rk9TQmXZfbqtdzBHWr8dTthSksrEtafXx09R738dFRUNh4QltYYMXHp/4ekD4+egoL695Lnp467rk3kCVLcph/WxajRpno3dud5cvzmTLFzPHjFm666Sg3XJ/JD9+rP4xmrt7BX8uvYo45o93dfIJdDFwjpQuciuzO2sJc3d3pfek0fvzPKrVDaXUuBjfG9b0ZY5YLCo7d65FbVjf51t/dt97jAR6+HC0+3vh55QX4e/jUe8zfw4fc8ro95ryNnjw94X7uWPc4VbU1TOs6hmHRffjrZ09wXa/LyCjOZu7792Ox1XLnwOuYED+seV/Yaaw6hcO57Wf47iSLBfT6XlitP5z74Bb254VXitLwsXOdg6JQV8e6zqBBHgwadCpB3LmzksOHLSxY4M+112Ry/98C8fXVcdttWXRLcmuQxLU2rVLDqKK/kGC6jGctszlW0z5WkcyPDMQovU9ORRKoVtBj7ER+/ewjyovaT6Xe8LBEBoVchpLVtiqKN7zBKWjOcYfT0PCuePpj4+KGMC5uiP3rLRk72Jd7iMdG3cGgV67k+UkPEejhy6TXb6JvRHKDhKy55IVVU51T3SJtO7qcExGYvdW7/smkpaDAip/fqY/doiIrPt6NJzQ+vjoKCur33BYWWRtNgmpqFJ57No/77gvkWJYFq1UhObluCDA83IV9qVX0H+AYq4PDyz7gUd0W3vZ6im9KjGqH06JCXQ3MDpVtW5yNpMOtwOBqpM+UmWqH0WoGplzOQI/JKPltJ3kKMNV9uJ3sOTopr6KQgLMkNAEevmc4p6jRJKi6toa/ffU0T4z5K+mFWVhtVvp36E6MXwc6+kawI3vvRb6SxqW75p/7ICeVqvIwXkiIHl9fHb9ur7Q/ZrEo7Pqtii5dGk8eEhON9c4B2L6tki5dzrwFyJtvFtKnjzud4lyx2uD06W7WWgWbg3X2GK3ZXFd8NX/1+gU3rfPWRVoQGSSbBjsh+Ym2kuRRY/H0D1A7jBZldPPisoF3E54fBRYH+6Q+hw7mEAI9fPkxfZv9sRqrha2Zv9ErrGuj5/UM68KP6b/Ue+yHw780es6zm1czLLov3YI7Y1Ws1NpO3eFqbbVYW+gOp2gUDuVntEjbbUHdMF7LrsarrLRx4EA1Bw7U9fJlH7dw4EA1J07UotFouOwyM//5TxEbN5Zz+HANS5fmYDRquGTEqTILTzyRw6uvnkrIL7vMzLZtlax5u4iMjBrWvF3Er79Wctk0c4Prp6fX8P2GMq6dU5e8d+hgQKOBzz8r4aefKsjIsNC5s2Puvdaz+AmeNCwh0QkXLYe5Grg6xPfcB4o2R4bwWolOb6D/tCv5avlzaofSIqKjetHHZxzKMcftdSqvqSC9MMv+dWZxNntO/IG3mxdhXkFcnzKD57e8SZRPOB19wnl+y5sYDa5MSRhlP+eOdY8T7OnPwqE3AXB9r+lM/8/tvPjTW4zuNIiv/tjIxiPb+ODqFxpcPy33MJ/s+5Yv57wGQIxvJFqNljW/rSPA5MfB/AySQ+Jb5LXnh1goL2i8HEN70NLDeGlp1dz111Nb5Lz8Ul0iNHq0iXvuDeTyK8xU19h47tk8SkttJCS48sSSENzdT/0dm5NTy+kdMV26GHnggUD+/e9CVq0qIDTUwAOLgkhIqN9rpSgKzzydyy23+OHmVteeq6uWe+4J4Lnn8rFYFBYs8MM/wHE/8n2qtrJQM4vPzctYUxyk1mLdZvd/kUG4SO+TU9Io7aUksQOwWa2s+ustFGY71zYaw/teQ2BBKNQ69q/SlowdzHz7/xo8Pr3rWJ6ZcH/dTWjTv3lr58cUV5XRPTSBx0b9hfiAaPuxM/5zO+HmYJ6ZcL/9sU/3beDJH18lo+gYkd6hZyyVoCgKl711G7f1m8XI2AH2x785sJkHvn6GGquFuwffwJXJLbMF0I5OJ9ie2fhqwvbAYICBA9/HamvfiWRbcNhzFs9WTyO3jfVk/1m40cCWvokYnHh4sj2TBKqVpW7cwGf/ekrtMJqFp8mPMcnz0B2TXyFH99/AbRSXFKsdhupGjkqnuvrHcx8oVFehj+J19yf4sdSgdigXbFnnCK6WyeNOS/oVW1n8gCH4R0SqHcZFi+80kAnRN0vy1AYUBdZK8vQ/uTnqF9UU58e9Np2bSq5kgXk3rm1w491IowuXB8vcJ2cmCVQr02i1DLh8ltphXDCNRsvoAfNItg1GKXXcwpjilEyzJE8npabq0GqdcKayk9Kg0K/oYZYanyXWrW0lUXdEBaGXoTunJgmUCjr17k9wTCe1w2gyb+8QZvRbiE+2L9ik56mtOFR+VO0QHEZNDRgMrbM3nmg+/hXfs6jyWqaZC859sAOI9zAyI0h6n5ydJFAqGXj5bLVDaJKkxBGMDZ+L5rhjVxQX9ZX5WsktyFM7DIeSm9NB7RDEBdArpVxWNI+HPL/AR+/Yt64n4sKl96kdcOzfQicWldyTyKQeaodxTjqdnvEDbyOhKgWlXIbs2pqjfqVqh+BwWmIYr7jYyvRp6Rw/7nhlPBY/fIL3/lukdhjNJq5kBU9oFtLX5Jh/zM0I9qGft+ncB4o2TxIoFY24/hb0Bhe1w2hUQEAU01PuxfOYibZelKWwspju/7qUzOLscx/cym76cBGv/PxOi7R9qMq5SmY0h7phvOb942XN20X06+dBcHDdirGRIw41+PfJJyX24zMza/jrnceYPu0I48YeZtasDF57rYDaP5UCWf9NKTfOO8qE8YeZOeMITy7Nobj4zInDd9+WMXLEIR5cVH/vxtmzvXnrrSLKy9t2SYDTmWrSmF96JTeZ/0DvQB09Zr2OB2NC1Q5DtBLHrarWDvgEh9J36kw2vfum2qE00CtpAp0sySg5ztHr9PyWNxkZM4AIcwgAEUuGNDjm76P/yuwekwE4mJ/BfV8t44+8dEqrywky+TE5cSR/GXgdBt2pt82qXz9g9fYPyCw5TphXEAv6z2Z617H12i2uKmXpDyv4Yv8PFFeVEWEOZtElt3FJTH8A7hg4h5lv/x9XJk/E07X5thup9LRxLNfxEkZHkJcbiafXpmZpq7raxuefl/L3vwfXe/zuuwPo3cfN/rWHx6m/V3U6DaNGe9Kpkwsmk5aDB2t4elkeig2uv6Fu7szu3VUsWVJXHLNff3fy8qw8+89cnl6Wy+JH6l/rxAkLy5fn061bw21homNcCQ7Ws359GZde6tUsr9kRaLEypGghcR7jeM56I0eq1U8QF0aHEODSdssuiKaRBEplvSdPI3XT9xRkZaodCgAGg5FxfW/GLcsVBcfsIm+qSks17+z6lNUzltZ7fNn4+xjWsY/9a0/XU93uep2eaV3G0C04Di9XE3tzDnDvF09iUxQWDr0RgNd3rGXJ96+wZOzdJIcksPNYKvd+uRSz0ZNRsQOBuu1grnrnr/i7e/PylEcJ8QzgWGkOJpdTQ0gJgTFEmIP5cO/XXNNjSrO97qygMpD542eUmqqnX383bLbKcx98Dj//XIlOB4l/2tPOZNLi63vmj9jQUAOhoadutEFBBn7bWcXu3VWnxVhFUJCeqZfVbdsSEmJgwkQv3n2nqF5bVqvC3/+ew7XX+rB7dxVlZQ0Tif4DPPjuW+dKoE4KLv+ch7W/8L7XU6wr8VQtjmRPN66Vmk/tigzhqUynNzDqhtvAAeqchIV25rLud+GW5Zj7ZV2oDYd+QqfVNdifzsvVRKDJz/7PzXDqdUd6h3J50ngSA2MJNwczutMgpiSO4ueju+zHfPD7l1zd/VIuTRhBpHcokxNHcEW3Cbz003/sx7yz6zOKqkp49bK/0zu8G+HmYPqEJ5EYGFsvllGxA/lo7zfN+rrTa4+f+6B2qrpawaWZVuPt3lVJXFzD98y//pXHZVPTufXWLD75pATbWVauZmVZ+OWXCpKSTyVhXboYycurZevWChRFobCglh9/KKdv3/rzt958oxBvs45x4xtPjuI7u7JvXzU1NW18LL4RLrY8riyew/1e3+Opa/3bmhZ4Ii4CrQN8jovWIz1QDiA8sStdho5gz4bmvYE2Rf+e04ks74SS53iTYC/W1szfSApuuMfcoq//yT1fLCXCHMIVSRO4uvsktJozf/geLjzK94e3Mjbu1NBfjdWCq67+HDajwZWd2alYrLUYdHq+PrCRXqFdeODrZ/jqj434unszJXEkt/a9Cp1WZz+ve0gCL/z0FtW1NbjqL35eXI1RISMn69wHtmO5zTSMd/x4LX5+9T9K51znQ88ebri4atjxayXLX86nuNjKrFk+9Y67fUEWf/xRg8WiMGGCJ3PmnHq+Sxcj990XyGOPnqCmRsFqhf4D3Jm/wN9+zO+/V/H556UsfyX8rDH6++uwWBQKC2sJCnLeIaYuxc+xxPV7VhgXsaO89ZKZWaF+9PCS+mLtjSRQDmLorLkc2v4zlaUl5z64GRmNJsam3Ixrlg4F9ecQtITMkuMEmep3rd81+HoGRfbCqHdl45HtPPrdCxRUFvF/A66td9yUN27h9xN/UG2t4erkSdw1+Hr7c0M69mHNrnWMiRtMt6A4dh1P451dn2Gx1VJQWUSQyZ+Momw2F+9gSuJIVs9YyuGCozzw9TNYbVbuGDjH3lawZwDV1hpyywsIN9ef33IhskMrsB1zzp9nc0lN1TXLMF51jYKLS/2b9emJUmxsXe/UG28WNkigHlgURGWFjYOHanhleT7/fbeYy6/wBuBIeg0vvJDPrNk+9E5xI7/AyivLC/jnM3ncdXcAFRU2nvhHDnfeGYDZrONsXFzr/jCoqnLOHqjTmat/486aq/jWvITVxR1a/FPNz6Dn/uiQFr6KcESSQDkIN08vhs6+ni9efKbVrhnVIZl+/pNQspyv1+l0VZZqXE31e3VOT5S6BNUVNX120+oGCdSLkx+mrKaSvTkHePy7l1j+8xpu6XuVvY3c8gImv3EzigL+Hj7M6DaWl7a+jU5Td0OzKTb83L1ZMvZudFodScGdOVGWx/Kf366XQBn/1+tUaamiOaST0yztOLPqanAx9KCqevNFtWM26ygtO/t8wYREIxXldcNwPqfNiwoMrPt/ZJQLNqvCM8/kMX2GGZ1Ow9tvF9Gli5HLL/cGIDoGjEYtf7njGNfN9aGw0Mrx47U88MCpodqTO5uOHnWIVasj7POsSkvr4vP2Pnui5Sy0Sg0ji/5CvOkynrXM5lhNy6VRi2JC8DbIrbQ9kp+6Azk5jJe5d3eLX2ton1kEF4ajFDp38gTg626muKrsrMf0DO1CaU05ueUFBHicqiAc6hUEQJx/FDbFxr1fPMmNvS9Hp9XhZnBl2fiFPDHmLvLKCwg0+fHWb59gcnHH171u4m+gyQ+DVl9vuK6TXyQ55QXUWC246OpucEVVdfWa/Ny9L/r1Wg0Kh3MyLrqd9iAvLxKT58UlULGxLqz/5uy/XwcOVOPiosHD1HgCo0C9MgZV1TZ0uvo9Wyen9ygKdOhgYMWr9Yfu/v1aAZWVNm69zZ+AgFMf7+mHawgI0J2zp8rZhJd9wKO6LazxepKvS9zOfUIT9TV7yH537ZgkUA5m5LzbeP3u+VhrW6Z8gIeHD2O734j+GLT54k7nqUtgJz7c+/VZj9lzYj+uehe8XBsvgKcoCrW2WpQ/fd8MOj0hXoEAfJy6nhExA+xzqVLCuvHR3m+wKTb7Y4cKMwk0+dmTJ4C03EOEeAbg2wwJ1PHQKmpPXPzvz5EjR9i8eTPHjh2jrKyMyy+/nPj4U3PJFEXh+++/Z/v27VRVVREWFsb48eMJDAw8a7t79+7lu+++o7CwEB8fHy655BISEhLsz+/atYv169dTU1NDjx49GD16tP25oqIi3njjDW688UZcXS9+scPevXr69Tdis114z19KijsrXy2gtNSKp6eOLZvLKSi0kphoxNVVw84dlbz2WgETJnjah/rWf1OKTq+hY0cXDAYNf/xRzcpXCxg2zGRPmvr38+Dpp3P5+OMSUlLcKCiw8uKL+cTHu+LvX/fR3bFj/Z5Vk0l7xsd3766iV6/2OUfHaM1mTvEsks338kJZXyqbaRsqvaau4rhGJo63W5JAORjf0HB6T57BT++/3extx8X0o6dpBMox56jtdL6GRvdhyQ+vUFRVirfRk68PbCK3rICeYV0w6l3ZkrGDpT+8ytXJk+wTuD/c8xV6rZ74gGhc9C7sPp7GE9+/wqT4S9Br6942hwoy2ZmdSo+QBIqqSlnxy7uk5R7mmQn32699TY/JrPr1fR765jmu6zWNw4VHeX7Lm1zXa1q9GLce3cWQqN7N8nqPGJpn65aamhqCgoLo3r077777boPnN23axJYtW5gyZQp+fn788MMPvPHGG8yfP7/R5CYzM5P33nuP4cOHk5CQQGpqKu+99x7XXXcd4eHhVFRU8MknnzB58mR8fHz4z3/+Q1RUFHFxcQB8+umnjBw5slmSJzh9GG/LBbcRHe1CXJwr328oZ+IkL3R6DR9/XMLLL+WjKBAcomfOtb5MnnJqlZxWp+GdNUUcPWpBUSAoSM+lk72YPt1sP2bMWE8qKm18tLaY5S/nYzJp6d7djXnzmtbjUVNjY+Omcp54on3P0+lRvIQnjX15UXcPeysuvr3rwwJIMDV/r5ZoOySBckB9p84kbfMPFGY30yoqjYaRfa/DLy8QpaR9JU8ACQExJAXHs27ft8zqPhm9Vs/rO9byyHfPY1MUOphD+OvguVzbc6r9HJ1Wx0tb/8OhwkwUBcK9gri251Ru6D3DfozVZuWVn9/hYEEGBq2e/pE9WDvrRXuxTqgbAnxr5jIWr3+e0a9dR5CnP3NTpnPr/+ZRAVTVVvPl/h95c+ZTF/1abTqFQ3nNM3zXqVMnOnU686bXiqKwdetWBg8ebO89mjJlCk899RS7d+8mJSXljOdt3bqVmJgYBg8eDMDgwYM5cuQIW7duJTw8nMLCQlxdXenata7kRMeOHcnNzSUuLo7du3ej0+nq9VY1h7z8KEymC0+gAGbN9mH5y/mMn+BJnz7u9Olz9t6e4cNNDB9+7u0+pk41M3Wq+ZzHnXTPvQ17/z7/rJSEeCOJiQ2LbLY3PlVbWaiZxRfmp3i7OPiC++CDXQzc3fHiF3uItk0SKAekNxgYecOt/PfRv110W2ZzEKO7zEWbbaO9DNmdyf8NuIbHvnuRq5InMTy6L8Oj+571+EsTRnBpwoizHtPJP4ovrlt5zmv3CuvKx9e83Ojza3Z9SvfQBHqGdTlnW+eSF1pDVW7zTEQ/m6KiIsrKyoiJibE/ptfriYqK4ujRo40mUJmZmfTr16/eYzExMWzduhUAX19fLBYL2dnZeHt7k5WVRffu3amsrOS7777j2muvPVOzFyV1r4G+/Vyx2aovuI2+fd3JOmohL89qnxjuKHR6DfMXSIHHk3RKJROKbiPR82qerZ5OrqXpE8wf7RSGSd++5pOJhhzrnS7sOnRNJnHwcPb++N0Ft9ElfhjdDANRsttfr9OfXRLTn8OFRzlemmufGO4oDFo9j468o1naOmIsaJZ2zqWsrG7StMlUvxfFw8OD4uLis57353NMJpO9PTc3N6ZMmcLatWuxWCwkJycTGxvLRx99RJ8+fSgqKmLNmjVYrVaGDRtGYmLiRb+Wqqq6opoXM4wHcNm08+8pak0TJzpf9fHm0LH0Lf6h/5HXPZfwQ+n51167LMiHSYHeLReYaDMkgXJgQ6+5gcM7tze5NpRWq2N0/xsxZ3uhVEvydNL1KTPOfZAKru5+abO0o2gUDhWou/pOUZrey/nncxISEuoN06Wnp5OTk8P48eN57rnnmDZtGiaTiVdffZXIyEg8PC5+/8DmGMYTbY9bbQY3lVxJd++HWF6STPU5fn/DXA38o1NYK0UnHJ1s5eLA3L3MjL75/5p0jp9fBDP6LMR8zKs9j9i1S4XBtZSWn305fXM52Yt0sufopIqKigY9TH8+78/nlJeXN3pObW0tn376KRMnTqSgoACbzUZUVBT+/v74+flx9GjzbPaXuteAVutcWxiJ89e3aDFLXf9JrFvjK+q0wHMJHTBLzSfxP5JAObjYlL4kjxp/Xsf26DqWUUGzoRmWsIu2J8NU2GrX8vb2xmQycejQIftjVquV9PR0wsMb31YkIiKi3jkAhw4dIiIi4ozH//DDD8TGxhISEoLNZsNmOzVfxWq1XlCP15nUDeP1aJa2RNvkX/kDiyqvZbr5zMPgN0UEMNBHvc2KheORBKoNGHrN9fiFd2j0eb3ehYmDbieuPBml8uwVkYXzOljSPL0xJ9XU1HD8+HGOH6+rdF1YWMjx48cpLi5Go9HQt29ffvzxR1JTU8nJyWHt2rUYDAa6detmb+PDDz/km29O7fHYt29fDh48yMaNG8nLy2Pjxo0cOnSIvn0bTurPyclhz549DB8+HAB/f380Gg2//vor+/fvJy8vj9DQ0GZ7vfn5HZutLdE26ZVSphbN4yHPz/HRn7o9JnoYuU+2axF/olGa60840aJyjxzmrb/didVSv3J4SHAnhkTMhDzpdWrPSvxrebfs+2ZtMz09ndWrVzd4PDk5mSlTptQrpFlZWUl4eHiDQpqrVq3C29ubKVOm2B/bu3cv3377LYWFhfj6+jYopAl186L+/e9/M2jQIHsNKID9+/fz2WefUVtbyyWXXELPnj2b7fUajdC337sXtRpPOI9yQxwrjY+xs0LPF73ipOaTaEASqDbk188+4rvVK+xf9+0xlY6V8SjVsmlse7enUwFbMneoHUabN2r0H1RV/aR2GMJB2NBhjP8Pg0PPXJZDtG8yhNeG9Bw/mY49UnBxcWfyoL8SVRQnyZMA4FBFMxVdbefy86PUDkE4kED/YZI8iUZJD1QbU1FcRNEbf2A72vLFEkXbUGG28p/qDWqH4RTc3DX07r0GRalROxShMlfXYPr2WYfB4KN2KMJBSQ9UG+Nu9sZ3XCf5yQm7owGtU7qgPaisUHB1ldV47Z1Go6Nrl2cleRJnJbfhNsgY443XyEi1wxAO4nBNttohOJUCWY3X7nXs+H94e8vQnTg7SaDaKM/hEbjGyV9H7V21h42jucfUDsOp7E11QaM5/609hHPx9RlIVOQtaoch2gBJoNoojUaD7+Wd0Znlg749OxZc0WzFJE9XUVHBk08+SVFRUbO3fbHeffddtmxpuW1XZBiv/XJx8Sexy9NoNHJrFOcmNenbMJ2HAd+rEsh9ZRdYZS1Ae3TYdrxF2t24cSNxcXF4e3sDsHjx4gbHTJgwgZSUumGOvLw8Pv30U3Jzc6mqqsLT05Nu3boxdOhQdLpTu9b//PPP/PLLLxQVFWE2mxk8eDDJyclnjOH333/n/fffp3PnzlxxxRX2x4cOHcrq1avp2bMnrq4ts/1KQX5H3D22tkjbwjFpNAa6dnkWVxd/tUMRbYQkUG2ca6QX5jFRFH92WO1QRCuzuCocyWne6uMAFouFHTt2cNVVV9V7fPLkycTGxtq/Pj150el0JCUlERISgtFo5MSJE3zyyScoisKIESMA+OWXX1i/fj2TJk0iLCyMrKwsPvnkE4xGI507d653raKiIr766is6dGhYgT8oKAhvb2927dpF7969m/Ol26WmupDS24CiWM59sHAK8Z0fwcenn9phiDZEEign4DkknJqsMip/y1U7FNGKjodWYs1u/q17Dhw4gFarbbA/ndFobHTTXx8fH3x8Ts3J8/b2Jj09nYyMDPtju3btolevXnTt2tV+ztGjR9m0aVO9BMpms/HBBx8wbNgwMjIyqKpqWLIjLi6O33//vcUSqIr/DeNVVf3cIu0Lx9Khww2Ehs5UOwzRxshAr5PwnRGHS6SX2mGIVpSuzWmRdo8cOXLGPeY+++wzli5dyooVK9i2bdtZ514VFBRw4MABIiNPrRa1Wq3o9fX/ZtPr9WRlZWG1nkoEv//+ezw8PM66TcvJHqza2pbbwqiwILrF2haOw99/JLEx96odhmiDpAfKSWj0WvyuSST3pd+ozatUOxzRwmr1CodzM1uk7aKiogY9TcOHD6djx44YDAYOHTrEV199RUVFBUOGDKl33MqVK8nOzsZqtdKzZ0/7RsAAMTEx7Nixg/j4eEJCQsjOzmbnzp3YbDYqKirw9PQkIyODHTt2cPPNN581Ri8vL6xWK2VlZfZ5Ws1t714ZxnN2nqYudO3yjEwaFxdEEignovMw4D+nCzkv7cRWLpsLO7Pc0GpqclqmWnZtbW2DnqLTE6Xg4GAAfvjhhwYJ1PTp06mpqeH48eN8/fXXbN68mYEDB9rbKCsrY+XKlSiKgslkIjk5mc2bN6PVaqmurubDDz9k0qRJuLu7nzXGk/FZLC2X3MgwnnNzdQkiKfkVdLqz/64J0RhJoJyM3t8Nv2u6kLtiN9TKPnnO6ohLXou17e7ufsZ5R6cLDw+nurqasrKyer1VZrMZgICAABRF4ZNPPqF///5otVoMBgOTJ09m4sSJlJeXYzKZ2L59Oy4uLri7u3PixAmKiop4++237e2dHCZ85JFHmD9/Pr6+vgBUVtb1snp4eDTra/+zwoKOuLlLAuVstFo3kpKWY3QNVjsU0YZJAuWEXCO98J0ZR8Hb+0CqGzgdm1bhUH7LDN9BXQ/Trl27znrM8ePH0ev1GI3GRo9RFAWbrWESr9Pp8PKqm6+3Z88e4uLi0Gg0+Pv7c8st9QsYfvvtt9TU1DB27Fh7cgaQk5ODl5fXOXuqLlZqqiu9UmQYz7lo6NJlGV5e3dQORLRxkkA5KfekAKyFVRR/nq52KKKZFYRYqMivaLH2Y2JiWL9+PZWVlbi5uZGWlkZZWRkRERHo9XrS09P59ttv6dmzp30obdeuXeh0OgIDA9Hr9Rw7doz169fTpUsXtNq6+SX5+flkZWURFhZGVVUVW7ZsIScnhylTpgB1w3KBgYH1YjmZoP358YyMDKKjW36Sd3m5gqtrd6qqfmnxa4nWERNzN4EBY9QOQzgBSaCcmOfQCGoLqijf2jLFFoU6jrgXQH7LtR8UFERoaCh79uwhJSUFnU7Htm3b+Oqrr1AUBR8fH4YNG0afPn3s52i1WjZt2kR+fj6KouDt7U3v3r3p37+//RibzcaWLVvIy8tDp9MRFRXF3LlzmzwJvLa2ln379jFr1qzmeslnVVgYjZubJFDOICRkOlGRN6kdhnASGqUl9oEQDkOxKeSv3kNVWqHaoYhm8m7AL5SUlrToNf744w+++uorbr31VjQaTYteq6l+/vln0tLSmD17dqtcz8NDQ6+Ut2UYr43z9u5Lj+6r0WoNaocinISs3XRyGq0G36sSMIS07GRb0ToKgywtnjwBdOrUiV69elFS0vLXaiqdTse4ceNa7Xp1w3hn3m5GtA1ubpEkdXtRkifRrKQHqp2wllST88JOrMUts/RdtI5dcbn8nHH2Cd6i+fXuXYPR7R21wxAXQK830zvlfdzdO6odinAy0gPVTui8XPG/risaV925DxYO61BpltohtEupqa5oNDJltK3R6TxITnpFkifRIiSBakcMwR74XZ0AWsea0yLOT6mvlbzCFpw9LhpVVibDeG2NTudOcvJKvL1T1A5FOClJoNoZY5wPvld0liSqDTrq53jzkdqToqIYtUMQ50mrdSM56VV8vFtms2khQBKodsk9KQDfK+NBJ0lUW3Ko8pjaIbRrqXuNMozXBmi1biQnr8DHp6/aoQgnJwlUO+XezR+/qySJaisqvaxk50k9LzXJMJ7j02qNJCe9gq9P/3MfLMRFkgSqHXPr4l83J0qSKIeXFViudggCKC6KVTsE0Qit1rUuefIdoHYoop2QBKqdc0v0w292IugliXJkhyzZaocggL17ZTWeI9JqXUnqthxf34FqhyLaEUmgBG7xvvjPTgS9/Do4oho3G5k5Ur7AEdQN4yWpHYY4jVbrQlK3l/DzG6x2KKKdkTumAMDY2Rf/axPRGORXwtEcC6lE6t06DhnGcxwajQvdur6In99QtUMR7ZDcLYWdsZMPfpJEOZx0TqgdgjhN6j5ZjecINBoXkrq9gL//cLVDEe2U3ClFPcZYH/zmdEHjIr8ajqDWRSE9J1PtMMRpSksUXF27qR1Gu6bRGOjW9V/4+1+idiiiHZO7pGjAGONdt+2Li2z7orYToVXU1taqHYb4k+LiTmqH0G7VJU/PERAwUu1QRDsnCZQ4I9eOZvzndpG981SWrs9VOwRxBvtSjWiQ90Zr0+k8SEp6mYCA0WqHIoQkUKJxrlFm/K/visYoNwo12HQKh3Iz1A5DnEFJiYKrUVbjtSZX12B69XwHf79haociBCAJlDgH1w5eBN6cjM7HVe1Q2p2c0Bqqq6vVDkM0oqRYVuO1FpMpgZSU9/H0TFA7FCHsJIES52QI9iDwtu64RHqpHUq7kmHMVzsEcRapqW4yjNcK/PyG0qvnOxhdg9UORYh6JIES50VnciFgXjfcuweoHUq7oGgUDubL8J0jqxvGk9V4LSks7GqSk1ag13uoHYoQDUgCJc6bRq/F94p4vEZFguz80qIKgmspr5D97xxdSYmsxmsZGmJj7yO+8yNoNNLLJxyTJFCiybxGdMD3yngpuNmCMkwFaocgzsO+VHfkY7R5abVGunV9gcgON6gdihBnJe98cUHckwIIuDEJraeL2qE4pYPFUjyzLSgutmGUYbxmYzD40bPHWwQGjlE7FCHOSRIoccFcIjwJnN8dQ4jMT2hOxQG1FJUUqx2GOE+lJXFqh+AU3N1j6Z3yPmZzd7VDEeK8SAIlLore7ErALckYE/3UDsVpZHpL8tSWpKa6IR+lF8fHux8pvf6Lm1uE2qEIcd7kXS8umtZFh9+sBExDw9UOxSkcLD+qdgiiCYqLFRnGuwjBwVPp3n0VBoOUSRFtiyRQollotBq8x3XEZ3on0MkSvQtV5mMltyBP7TBEE5XKarwm02j0xMbeR5fEp9BqDWqHI0STSQIlmpVHSjAB13dD66FXO5Q2Kcu/VO0QxAXYt88D+Tg9f66uIfTq+bastBNtmrzjRbNzjTYTdHtPXDpKl3xTHarOVjsEcQGKimwYjV3VDqNN8PMbRt8+n2A291Q7FCEuiiRQokXozK4EzEvCc0QHKbp5nqpMVrJyjqkdhrhApaWyGu9sNBodMdF3k5z0KgaDj9rhCHHRJIESLUaj1WAeFUnAvG7ovKRe1LlkBUnl8bZMimo2ztUliB493iIq6mY0GvmLSjgHebeLFuca7U3g//XEGO+rdigOLd16Qu0QxEUoKlIwGruoHYbD8fMbTp8+n+Dj3VvtUIRoVpJAiVah8zDgd20i5gnRskrvDGqMCkdypHxBW1da2lntEByGVutKXNxDdE9+FRcXqRMnnI8kUKLVaDQaPAeHEXhbd/RB7mqH41COh1Rgs9nUDkNcpLR9HsikPzB5dKZ3yodEhF+jdihCtBhJoESrcwk1EbSgB6ZBYXKv+Z90bY7aIYhmUFgoq/HCw68lJeVDTCbpjRPOTYr1CFVo9Fq8J0ZjTPCl8N39WIur1Q5JNVaDwuEc2TzYWZSVdUav3612GK3OYPAjMXEp/n7D1A5FiFYhPVBCVcYYb4L+0hP37gFqh6KaE6HVWCwWtcMQzSRtnzvtrWs1OGgK/fp+LsmTaFekB0qoTmvU43tFPMZEP4o+OoitvH0lE0cMsnWLMykoqFuNV1X1u9qhtDh39450jluMr+9AtUMRotVJAiUchntSAMZYb4q/OkL51mxQ1I6o5dm0CofyMtQOQzSzumE8502gtFoXIjvcTFTUzWi1rmqHI4QqZAhPOBStuwGfKbEE3tYdlwhPtcNpcXmhFiqrKtUOQzQzZ16N5+PTn759PiM6+v8keRLtmiRQwiG5hHsScGsy3pfFonV33o7SDLd8tUMQLaBuGC9R7TCaVd0k8WX07PEm7u4d1Q5HCNU5751JtHkajQZTnxDcuvhT8mU65b8cd6phPUWjcLBQVt85q/KyeHT6PWqH0Qw0hIbOJDbmXgwGs9rBCOEwNIqiONEtSTizmsxSCtcewJJVpnYozaIg2MIHRT+oHYZoIX5+WhK7vE5bzvo9POKIj38Mb3MvtUMRwuHIEJ5oM1wiPAm8rTveU2LQuLX9ztNMzyK1QxAtKD/f1maH8bRaN2Jj7qFP709UTZ7mzJmDRqOx//Pz82Ps2LHs2rVLtZiEOEkSqHZCo9Gwdu3aRp/fsGEDGo2GoqKiVovpQmi0Gkz9Qgn+ay/cewW16Xm6B0tl+M7ZlZfFqx1Ck/n5Dadf3y+IjLwJrVb9P1TGjh1LdnY22dnZrF+/Hr1ez8SJE9UOSwhJoJxFTk4ON910Ex06dMDV1ZXg4GDGjBnDli1bzuv8AQMGkJ2djdl89jkOc+bMYcqUKc0Q8cXRmVzwnRFHwM3JGEI81A6nyUr8rBQUFaodhmhhaWltZzWep2c3uie/RvfkV3FzC1c7HLuTn2fBwcF0796de++9l8zMTHJzcwG49957iYuLw93dnejoaBYtWtSgMO1jjz1GYGAgnp6e3HDDDSxcuJDu3bur8GqEM1H/zwvRLKZNm4bFYmH16tVER0dz4sQJ1q9fT0FBwXmd7+LiQnBwcKPPW61WNBrHuxG4RnoRuKAH5T9nU/ptJtaSGrVDOi9HfYuhXO0oREvLz1cwGuOpqkpVO5RGmTw6Ex19BwEBo9UO5ZzKysp46623iI2Nxc/PDwBPT09WrVpFaGgou3fvZt68eXh6enLPPfcA8NZbb/H444/z4osvMnDgQNasWcOyZcvo2FFWEoqLI5PInUBRURE+Pj5s2LCBoUOHnvEYjUbDihUr+PTTT/nyyy8JCwtj2bJlXHrppUDdEN7w4cMpLCzE29ubVatWcccdd/Dmm29yzz33sH//fq6++mpef/31eu1+9913DBs2rKVf4nlRam2U/3yc0g2On0itC/+d43kn1A5DtIIBA8vR6T5QO4wG3N070jHqdoKCJqLROOZgxJw5c3jzzTcxGo0AlJeXExISwrp16+jZs+cZz3nyySd555132LZtGwD9+vUjJSWF559/3n7MoEGDKCsrY+fOnS3+GoTzcsx3jWgSk8mEyWRi7dq1VFc3vinv4sWLmTlzJrt27WL8+PFcffXVZ+2hqqio4B//+Aevvvoqe/bs4bnnnmPmzJn15iQMGDCgJV7SBdHotZgGhBJ8T2+8J0Wj9XJRO6QzqjDbJHlqR/anmdQOoR6jMYyE+Cfo1/dLgoMvddjk6aThw4ezc+dOdu7cydatWxk9ejTjxo3jyJEjALz33nsMGjSI4OBgTCYTixYtIiPjVHX/tLQ0+vTpU6/NP38txIVw7HeOOC96vZ5Vq1axevVqvL29GThwIPfff3+DlSpz5szhyiuvJDY2lr///e+Ul5fz888/N9quxWLhxRdfZMCAAXTu3Bmz2Yybm1u9OQkuLo6XpGj0WkwDwwhx0EQqK6BU7RBEK8rLUzAaE9QOA1eXIDrHLaZ/v28IDZ2BRqNTO6Tz4uHhQWxsLLGxsfTp04eVK1dSXl7OihUr+Omnn7jiiisYN24c69atY8eOHfztb3+jpqZ+D/Sfpx/IwItoDpJAOYlp06Zx7NgxPv74Y8aMGcOGDRvo2bMnq1atsh+TlJRk/7+Hhweenp7k5OQ02qaLi0u9c9oaR02kDlmy1Q5BtLKKcvUSKIPBl9jY++jf/1vCw2eh1TrG++BCaTQatFotlZWVbNq0icjISP72t7+RkpJCp06d7D1TJ3Xu3LnBH4onh/eEuBgyidyJGI1GRo0axahRo3jwwQe54YYbeOihh5gzZw4ABoOh3vEajQabzdZoe25ubg45cbypTiZSHn1CKPs5m9Lvj2JTaY5UtbvC0ZxjqlxbqGf/fk/iWzmH0uu96NDhBiLC56DXt72VqidVV1dz/PhxAAoLC3n++ecpKytj0qRJFBcXk5GRwZo1a+jduzeffvopH374Yb3zFyxYwLx580hJSWHAgAG888477Nq1i+joaDVejnAikkA5scTExLPWfroQLi4uWK3WZm2ztWgMWjwHhmFSMZE6FlyOckyGD9qb3Fwb3XvEU1W1r8WvpdOZiAi/hg4d5mEweLX49VraF198QUhICFC34i4+Pp7//ve/9sUrf/nLX5g/fz7V1dVMmDCBRYsW8fDDD9vPv/rqqzl06BB33XUXVVVVzJw5kzlz5px1+oIQ50MSKCeQn5/PjBkzmDt3LklJSXh6erJt2zaWLl3K5MmTm/VaUVFRfPnll6SlpeHn54fZbG7Qs+Xo1Eyk0pHJ4+1VRXkCWl3LJVAeHp0ID5tFcPDUNt3jdLpVq1bVm4ZwJkuXLmXp0qX1Hrvjjjvqfb1o0SIWLVpk/3rUqFHExsY2V5iinZIEygmYTCb69u3LM888w8GDB7FYLERERDBv3jzuv//+Zr3WvHnz2LBhAykpKZSVlTlUGYOmsidSfUOo3JNH2U/HqTlc3GLXs7gqpJ+Q6uPt1f79Xs0+jKfR6PD3H0l4+Gx8ffo3b+NOoqKigpdffpkxY8ag0+l4++23+eabb/j666/VDk20cVIHSojTWHIqKN+aTfmvOSiVtc3admbHCr7MPr/K8MI5jRr9S7MM4xkMfoSFXk5Y2JUYjaHNEJnzqqysZNKkSfz6669UV1fTuXNnHnjgAS677DK1QxNtnCRQQpyBYrFS8Vse5VuzqclsnrIDG2OOsC/rQLO0JdqmgYPK0Go/PPeBjfDy6k54+GyCAse3+dV0QrR1kkAJcQ41x8oo35pNxY5clJoLm0Bv1Su85b6xQX0a0b4EBGiIT3j93AeeRqt1JShwIuHhs/DyartlRYRwNpJACXGebNW1VOzIpXxrNpbspm1kdzyiinW5m1ooMtGWjBr9M1VVaec8zmgMIyzsasJCZ2Iw+LRCZEKIppBJ5EKcJ62rHlO/EEz9QqjOKKH8p2wqd+ehWBqvpXXSEWN+K0Qo2oKKikS02jMnUFqtEX//SwgOnoK/33CH32ZFiPZMeqCEuAi2CgsVv+VSuTuP6sPFcIZ3k02rsMb8ExWVFa0foHA4gYFaOsevtn+t0Rjw9R1EcNAk/P1HOk0JAiGcnSRQQjQTa2kNlXvyqNyVR3V6MfyvYyovtIa1BT+qG5xwKKNG/4Kb0ZugoEkEBo7FYPBWOyQhRBNJAiVEC7CW1VD5ez6Vu3P5Sbef7Rm/qx2SUJlWqyUqKoqEhAQSEuIwmcxqhySEuAiSQAnRwioqKti/fz9paWkcPHhQVuK1IwaDgZiYGBISEoiLi8PNzU3tkIQQzUQSKCFaUW1tLenp6aSlpbF//36Ki1uu8rlQh5eXFx07diQ+Pp7Y2Ng2t9WREOL8SAIlhIqOHz/OH3/8wZEjR8jMzKS6ulrtkEQTmc1moqKiiIyMJCoqCl9fX7VDEkK0AkmghHAQNpuNnJwcMjMzycjIICMjQ3qoHJCPj489WYqKisLb21vtkIQQKpAESggHVlxcTEZGhj2pOnHiBPKWbV1+fn72hCkyMhKzWSZ/CyEkgRKiTamqquLo0aP2HqqsrCwsFovaYTkNvV6Pv78/4eHh9qTJ09NT7bCEEA5IEigh2jCr1crx48c5ceIEeXl55Ofnk5+fT0FBATbbuSukt1cGg4GAgIAG/7y9vdFqpfq3EOLcJIESwglZrVaKiorqJVUn/19WVqZ2eK3GxcWl0URJo9GoHZ4Qog2TBEqIdqaqqqpBUlVQUEB5eTkVFRXU1taqHeJ50Wq1eHh4NPjn5eWFv78/gYGBMl9JCNFiJIESQtRjsViorKyksrKSiooK+/8be+zk11ar9YKup9Vq0Wq16HQ69Ho97u7u9mTo9P//+Z8UpRRCqEkSKCFEs7BYLCiK0uAfYP+/RqNBp9PZEyatVitDaUKINkkSKCGEEEKIJpLlJkIIIYQQTSQJlBBCCCFEE0kCJYQQQgjRRJJACSGEEEI0kSRQQgghhBBNJAmUEEIIIUQTSQIlhBBCCNFEkkAJIYQQQjSRJFBCCCGEEE0kCZQQQgghRBNJAiWEEEII0USSQAkhhBBCNJEkUEIIIYQQTSQJlBCiXUpPT0ej0bBz5061QxFCtEGSQAkhWl1OTg433XQTHTp0wNXVleDgYMaMGcOWLVvUDk0IIc6LXu0AhBDtz7Rp07BYLKxevZro6GhOnDjB+vXrKSgoUDu0i2KxWDAYDGqHIYRoBdIDJYRoVUVFRWzcuJElS5YwfPhwIiMj6dOnD/fddx8TJkwAQKPR8OqrrzJ16lTc3d3p1KkTH3/8cb129u7dy/jx4zGZTAQFBTF79mzy8vLsz3/xxRcMGjQIb29v/Pz8mDhxIgcPHmw0LpvNxrx584iLi+PIkSMAfPLJJ/Tq1Quj0Uh0dDSLFy+mtrbWfo5Go+Hll19m8uTJeHh48NhjjzXnt0oI4cAkgRJCtCqTyYTJZGLt2rVUV1c3etzixYuZOXMmu3btYvz48Vx99dX2Hqrs7GyGDh1K9+7d2bZtG1988QUnTpxg5syZ9vPLy8u58847+eWXX1i/fj1arZapU6dis9kaXKumpoaZM2eybds2Nm7cSGRkJF9++SWzZs3i9ttvZ+/evSxfvpxVq1bx+OOP1zv3oYceYvLkyezevZu5c+c203dJCOHwFCGEaGXvvfee4uPjoxiNRmXAgAHKfffdp/z222/25wHlgQcesH9dVlamaDQa5fPPP1cURVEWLVqkjB49ul6bmZmZCqCkpaWd8Zo5OTkKoOzevVtRFEU5fPiwAig//vijMnLkSGXgwIFKUVGR/fjBgwcrf//73+u18cYbbyghISH14rzjjjsu8LsghGjLpAdKCNHqpk2bxrFjx/j4448ZM2YMGzZsoGfPnqxatcp+TFJSkv3/Hh4eeHp6kpOTA8D27dv57rvv7L1ZJpOJ+Ph4APsw3cGDB7nqqquIjo7Gy8uLjh07ApCRkVEvliuvvJKysjK++uorzGaz/fHt27fzyCOP1LvGvHnzyM7OpqKiwn5cSkpK835zhBBtgkwiF0Kowmg0MmrUKEaNGsWDDz7IDTfcwEMPPcScOXMAGkzG1mg09uE3m83GpEmTWLJkSYN2Q0JCAJg0aRIRERGsWLGC0NBQbDYbXbt2paampt7x48eP58033+Snn37ikksusT9us9lYvHgxl1122RljP8nDw+PCvgFCiDZNEighhENITExk7dq153Vsz549ef/994mKikKvb/gxlp+fT2pqKsuXL2fw4MEAbNy48Yxt3XLLLXTt2pVLL72UTz/9lKFDh9qvkZaWRmxs7IW9ICGEU5MESgjRqvLz85kxYwZz584lKSkJT09Ptm3bxtKlS5k8efJ5tXHbbbexYsUKrrzySu6++278/f05cOAAa9asYcWKFfj4+ODn58crr7xCSEgIGRkZLFy4sNH2FixYgNVqZeLEiXz++ecMGjSIBx98kIkTJxIREcGMGTPQarXs2rWL3bt3y2o7IYQkUEKI1mUymejbty/PPPMMBw8exGKxEBERwbx587j//vvPq43Q0FA2bdrEvffey5gxY6iuriYyMpKxY8ei1WrRaDSsWbOG22+/na5du9K5c2eee+45hg0b1mibd9xxBzabjfHjx/PFF18wZswY1q1bxyOPPMLSpUsxGAzEx8dzww03NNN3QgjRlmkURVHUDkIIIYQQoi2RVXhCCCGEEE0kCZQQQgghRBNJAiWEEEII0USSQAkhhBBCNJEkUEIIIYQQTSQJlBBCCCFEE0kCJYQQQgjRRJJACSGEEEI0kSRQQgghhBBNJAmUEEIIIUQTSQIlhBBCCNFEkkAJIYQQQjSRJFBCCCGEEE0kCZQQQgghRBNJAiWEEEII0USSQAkhhBBCNJEkUEIIIYQQTSQJlBBCCCFEE0kCJYQQQgjRRJJACSGEEEI0kSRQQgghhBBNJAmUEEIIIUQTSQIlhBBCCNFEkkAJIYQQQjSRJFBCCCGEEE0kCZQQQgghRBNJAiWEEEII0USSQAkhhBBCNJEkUEIIIYQQTSQJlBBCCCFEE0kCJYQQQgjRRJJACSGEEEI0kSRQQgghhBBNJAmUEEIIIUQTSQIlhBBCCNFEkkAJIYQQQjSRJFBCCCGEEE30/5lFdGpb5d9jAAAAAElFTkSuQmCC",
+ "text/plain": [
+ "<Figure size 1000x700 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 1000x700 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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xMQAgOTkZHA4HSUlJNvu8/fbbiIqKAsMwAICEhARMnz4dMpkMwcHBWLFiBeRyubX9hAkTsHbtWqxbtw4BAQGIj49v3Q+CEEKcGCVRhLgAiUQCo9HY5n4YhsHcuXNRXl6OI0eO4J9//kF6ejoWL14MAOjevTsGDx6MHTt22Oy3c+dOLFu2DBwOB4WFhRg/fjwGDBiAc+fOYe/evSguLsaiRYts9vnqq6/A5/Nx/PhxfPzxx22OnRBCnA1N5xHi5M6cOYOdO3di8uTJbe5r//79uHLlCjIzMxEZGQkA+Oabb9C7d2+cPXsWQ4cOxfLly/HBBx/glVdeAQCkpKTg/Pnz+PrrrwEA27dvx6BBg/Daa69Z+/38888RGRmJlJQUdOvWDQDQpUsXbN26tc0xE0KIs6KRKEKc0O+//w6ZTAaxWIyRI0di3LhxeP/999vcb2JiIiIjI60JFAD06tULPj4+SExMBAAsWbIE2dnZOHXqFABgx44dGDBgAHr16gUAOH/+PA4dOgSZTGb906NHDwBAenq6td8hQ4a0OV5CCHFmNBJFiBOaOHEitm/fDoFAgLCwMAgEAgAAl8u1rku6qSXTfAzDgMPh3HJ7aGgoJk6ciJ07d2LEiBHYtWsXHnjgAWtbi8WCWbNmYcuWLfX6CQ0Ntf5bKpU2Oy5CCHFFlEQR4oSkUim6dOlSb3tgYCCuXbtms+3SpUvWJKspvXr1Qk5ODnJzc62jUQkJCVAqlejZs6e13fLly/HMM89g6dKlSE9Px5IlS6zPDRo0CLt370ZUVBT4fDqFEEI6LprOI8SFTJo0CefOncPXX3+N1NRUrF+/vl5SdStxcXHo168fli9fjgsXLuDMmTO48847MX78eJvpt3nz5kGlUuHBBx/ExIkTER4ebn1uzZo1KC8vx9KlS3HmzBlkZGRg3759uOeee2A2m+36egkhxJlREkWIC5k6dSpeeuklPP300xg6dCgqKytx5513Nnt/DoeDn3/+Gb6+vhg3bhzi4uIQExOD77//3qadl5cXZs2ahcuXL2P58uU2z4WFheH48eMwm82YOnUq+vTpg0cffRTe3t7gcumUQgjpODhM3QUWhBBCCCGkSfS1kRBCCCGkFSiJIoQQQghpBUqiCCGEEEJagZIoQgghhJBWoCSKEEIIIaQVKIkihBBCCGkFSqIIIYQQQlqBkihCCCGEkFagJIoQQgghpBUoiSKEEEIIaQVKogghhBBCWoGSKEIIIYSQVqAkihBCCCGkFSiJIoQQQghpBUqiCCGEEEJagc92AIQQ92GymGBhLNa/zYwZZsYMC2MBAHA5XPA4PPA4vOp/c6v/zefSqYgQ4nrozEUIqUdn0kGhU6BcX44KXQUUekW9vxU6BSr0FVDoFFAalDBZTG06Jp/Dh5fICz4iH/iIfOAn9oOP2Ae+Il/4iHzgK67521fsC1+RLzwEHnZ6xYQQ0nIchmEYtoMghLQfk8WEnMocZCozrX9KNaXVCdGNJEln1rEdZrOIeCJ4i7yrEy2xDwIkAYjyikKMdwyivaMR5RUFAU/AdpiEEDdFSRQhbkpj1CBTmYkMZYb17wxlBnIrc9s8auQqeBwewmXh1UmVTzSivaIR4xODGO8YeAo92Q6PEOLiKIkixMXJtXJkVGTUS5iKNcVsh+bUAiQBiPaOto5a3fx3iDSE7dAIIS6CkihCXAjDMEitSMWF4gs4X3weF4ovoERbwnZYbsVP7IeBQQMxKGgQBocMRg/fHuBxeWyHRQhxQpREEeLETBYTEsoSrAnTxdKLUOqVbIfVoUgFUgwIHIBBwYMwKGgQ+gX2g5AnZDssQogToCSKECeiM+lwufSydaTpivwKtCYt22GRWoRcIfoE9MHg4MEYFDwIAwIHQCaUsR0WIYQFlEQRwiKVQYWLxRdxvuQ8zhefR0JZQodZ9O0ueBweuvl2w+DgwdbEyk/sx3ZYhJB2QEkUIe0svyofh3IO4WDuQVwsvggTQ0mTO+GAg76BfTEpchImdZqEaO9otkMihDgIJVGEtIPEskQczD2IQzmHkKxIZjsc0o6ivKIwsdNETIqchP6B/cHhcNgOiRBiJ5REEeIADMNAe/Eizuefxmb9zyhQF7AdEnECAZIArI2ahfn+A4HocQBd9UeIS6PbvhBiR9rLl6H68y+o/v4bpqIi+PXthoKZlECRanKtHLL0w8CfGwFpINBzNtD7dqDzaIBL94MnxNXQSBQhbaS9fh2Vf/0F1V97YczPt32Sw8FzTwYinV/OTnDEqYh5IhzJKYCHQW37hCwE6DUH6DMPiBwO0JQfIS6BkihCWsFcUYGKPT+h4n//gyEr65ZtL94xBK9HXmqXuIhzm+TbC+9e2HvrRt6RwMAVwOC7AE+qnk6IM6PpPEJaQHv1KhQ7dkL1119g9Ppm7dP3uhqIdHBgxCXEawxNN1LmAodfA46+CfSYAQxdBUSPdXxwhJAWo5EoQppg0emg+uNPKHbtgu7atZZ3wONh3Tof5PGp0nhHJuAKcCRfDk9dK34PAnsAQ+4F+i8BxF72D44Q0iqURBHSCEN2NhS7voPyp59gVrYtATq7cgjeDL1kn8CISxrr0xMfXfy7bZ0IZUDfhdWjUyF97BMYIaTVaDqPkFoYsxlVhw9DsXMX1CdOAHb6jtH/ug4ItUtXxEXF681t78RQBZz/ovpP5IjqZKrXHIBP9/IjhA10Ta2LWLlyJTgcDjgcDgQCAYKDgxEfH4/PP/8cFouF7fBcnkkuh3z7dqTFxSNvzVqojx+3WwIFAMKLSQiySO3WH3EtfA4fE7Mu2LfT3FPAnlXAf3oB+zcCFbn27Z9FN891jf1ZuXIl2yESAoCm81zGypUrUVxcjC+++AJmsxnFxcXYu3cvXn/9dYwdOxa//vor+Pz6A4tGoxECgYCFiF2D9soVlH/5FSr/+QeM0ejQYx2/ezDeDbns0GMQ5zTcuxs+u7TfsQfhcIGuU4ERDwIx4x17LAcrKiqy/vv777/Hyy+/jOTkmkr/EokE3t7e1sfOep4zGAwQCmmU0J3RSJQLEYlECAkJQXh4OAYNGoTnn38ev/zyC/766y98+eWXAKq/wf33v//FnDlzIJVKsXnzZgDAb7/9hsGDB0MsFiMmJgYbN26EyVRzz7YNGzagU6dOEIlECAsLwyOPPGJ97qOPPkLXrl0hFosRHByMBQsWtOvrdgTttevIeeABZC1aDNWffzo8gQKAwUmOPwZxTlNM7XCqZSxAyl/A17OBL6YDWf86/pgOEhISYv3j7e0NDodjfazT6eDj44P//e9/mDBhAsRiMb799ltYLBZs2rQJEREREIlEGDBgAPburSkncfjwYXA4HFRUVFi3Xbp0CRwOB1k3ypRkZ2dj1qxZ8PX1hVQqRe/evfHnn39a2yckJGD69OmQyWQIDg7GihUrIJfLrc9PmDABa9euxbp16xAQEID4+HiH/6wIuyiJcnGTJk1C//79sWfPHuu29evXY86cObh69Sruuece/P3337jjjjvwyCOPICEhAR9//DG+/PJLvPrqqwCAH3/8Ef/5z3/w8ccfIzU1FT///DP69u0LADh37hweeeQRbNq0CcnJydi7dy/GjRvHymu1B11yMnLXrEXWggVQHznarseWXEiGr0XSrsck7ONyuJiUdal9D5p9HPhyBvDVLCDndPseu50888wzeOSRR5CYmIipU6fi3XffxVtvvYVt27bhypUrmDp1KmbPno3U1NRm97lmzRro9XocPXoUV69exZYtWyCTyQAAhYWFGD9+PAYMGIBz585h7969KC4uxqJFi2z6+Oqrr8Dn83H8+HF8/PHHdn3NxPnQwnI30KNHD1y5csX6eNmyZbjnnnusj1esWIFnn30Wd911FwAgJiYGr7zyCp5++mmsX78eOTk5CAkJQVxcHAQCATp16oRhw4YBAHJyciCVSjFz5kx4enqic+fOGDhwYPu+QDvQp6Wh9P0PULlvn13XOrUEo9djSVlvbA+80nRj4jYGeMUgIOMgOwfPPApkTgFiJwOTXgDCB7MThwM89thjmDdvnvXxtm3b8Mwzz2DJkiUAgC1btuDQoUN455138OGHHzarz5ycHMyfP9/6JTImJsb63Pbt2zFo0CC89tpr1m2ff/45IiMjkZKSgm7dugEAunTpgq1bt7b59RHXQCNRboBhGJs7ww8ZMsTm+fPnz2PTpk2QyWTWP/fddx8KCwuh0WiwcOFCaLVaxMTE4L777sNPP/1kneqLj49H586dERMTgxUrVmDHjh3QaDTt+vraQp+ZifwnnkTG7Dmo/Ptv1hKom4Yl00UAHU282QnWxKQfAD6dBOxcDBS6x7q82uc5lUqFgoICjB492qbN6NGjkZiY2Ow+H3nkEWzevBmjR4/G+vXrbb6cnj9/HocOHbI5j/bo0QMAkJ6e3mBcxP1REuUGEhMTER0dbX0sldpeBWaxWLBx40ZcunTJ+ufq1atITU2FWCxGZGQkkpOT8eGHH0IikeChhx7CuHHjYDQa4enpiQsXLmDXrl0IDQ3Fyy+/jP79+9usK3BGhpwcFDzzLDJmzoLqjz8AJ7mCUXYuGZ4WEdthkHbCAQdxua0o0OooKXuBj8cD3y0Hiq+zHU2b1D3PAbD5MgnYfsHk3rjBc+1rqYx11kKuWrUKGRkZWLFiBa5evYohQ4bg/fffB1B9Hp01a5bNefTSpUtITU21WeLQUFzEfVES5eIOHjyIq1evYv78+Y22GTRoEJKTk9GlS5d6f26eWCQSCWbPno333nsPhw8fxsmTJ3H16lUAAJ/PR1xcHLZu3YorV64gKysLBw+yND3RBGN+PgpefBHp02dA+csvgNkOtXnsiNFqsVDRhe0wSDvp6xWNkIo8tsOogwGSfge2jwZ+WAmUJje5h7Pz8vJCWFgY/v3XdjH9iRMn0LNnTwBAYGAggOq1TTddunSpXl+RkZFYvXo19uzZgyeeeAKffvopgOrz6PXr1xEVFVXvPEqJU8dFa6JciF6vR1FRUb0SBzNnzsSdd97Z6H4vv/wyZs6cicjISCxcuBBcLhdXrlzB1atXsXnzZnz55Zcwm80YPnw4PDw88M0330AikaBz5874/fffkZGRgXHjxsHX1xd//vknLBYLunfv3o6vvGnGoiLI//tfKHfvaZcr7dpidCoXn/uzHQVpD3GMM19IwADXfwISfgH6LAAmPAv4x7IdVKs99dRTWL9+PWJjYzFgwAB88cUXuHTpEnbs2AGgeq1SZGQkNmzYgM2bNyM1NRVvvfWWTR+PPfYYpk2bhm7dukGhUODgwYPWJGzNmjX49NNPsXTpUjz11FMICAhAWloavvvuO3z66afg8Xjt/poJ+yiJciF79+5FaGgo+Hw+fH190b9/f7z33nu46667rCNKDZk6dSp+//13bNq0CVu3boVAIECPHj2watUqAICPjw/eeOMNrFu3DmazGX379sVvv/0Gf39/+Pj4YM+ePdiwYQN0Oh26du2KXbt2oXfv3u31sm/Jolaj9MOPoPj2WzCGZtzc1Ql4nUuFeDgfOo6p6cbEpcXlJ7AdQtMYC3D1f8C13cDA5UDcRsDDj+2oWuyRRx6BSqXCE088gZKSEvTq1Qu//vorunbtCgAQCATYtWsXHnzwQfTv3x9Dhw7F5s2bsXDhQmsfZrMZa9asQV5eHry8vHDbbbfhP//5DwAgLCwMx48fxzPPPIOpU6dCr9ejc+fOuO222255/iXujYptEpel+nsfil9/HaZahflcxa8P9sO3Pi7wAUtarYdnZ/xw5RjbYbScxA+I3wgMXAHUWWNECLFF6TNxOYbcXOTcfz/yH33UJRMoABiX5nzVlYl9xXE82Q6hdbTlwK8PA59PdfnF54Q4GiVRxGUwBgPk27cjY9ZsqI+64Df8WnzPpkHI0BoKdxZfmMJ2CG2Texr4eBzw9wuAvortaAhxSpREEZegPnkSGbPnoPTd98DodGyH02ZMhRJzK7uyHQZxkFhZBGJK0tgOo+0sJuDkB8CHw6oXoBNCbFASRZyaqbQU+U88iZy774Hhxv2t3MXEdDHbIRAHieP5sh2Cfanygf/dCXy7ACjPZDsaQpwGJVHEKTEWC8q/3YH06TOqi2W6oYCzGeCBFu66o/giN0000v4BPhoJHHkTMLnG1bCEOBIlUcTpaK9eQ9bCRSjevBmWykq2w3EYpqwcMyqp8Ka7ifQIQfciN77y0qQFDm0Gto8CMo6wHQ0hrKIkijgNs0qFwo0bkbV4MXTXO8ZVQfGZMrZDIHYWJwxkO4T2UZYKfD0b2L0KqCxmOxpCWEFJFHEKVUeOIH3GDFTs+s5p7nPXHkLOZYNDldrcypSSHLZDaF9XfwA+GApc/p7tSAhpd5REEVZZdDoUbdqE3AdWw1wqZzucdscUl2CqxnVvtUFshUoC0Sf/KtthtD+9EvjpfuDHewBtBdvRENJuKIkirNElJCBz/gIodu5iOxRW3ZbtzXYIxE4mi0LZDoFd13ZX39g407XruBHSXJREkXbHWCyQf/opMhcvgSE9ne1wWBd+LpftEIidxJcVsB0C+1R51Wul/nkZMDv3zcAJaStKoki7MhYWImfl3Sh9623ASCdYAGDyCzFB25ntMEgbBYj8MCD3EtthOAfGAhx/F/h0ElDq4pXbCbkFSqJIu6ncvx8Zc2+H5swZtkNxOjNy/NkOgbTRZEk4uEzHuSiiWYquVN865sLXbEdCiENQEkUcjjEYUPTqa8hb+zAsSiXb4TilqAs0DeTq4itK2A7BOZm01Tc03nM/3YOPuB1KoohDGfLykLVsORTffMN2KE6NycrDSF0E22GQVvIVemNI1gW2w3BuV74HPpkAFF1jOxJC7IaSKOIwqr/3IfP2edBdo5Nmc8zOC2I7BNJKE6WdwGPMbIfh/MpSgc8mA+e+YDsSQuyCkihidxaDAUWbXkH+o4+69W1b7C32Ek0Huao4ZTnbIbgOkw74/bHqmlJ6Oj8Q10ZJFLErU2kpsu9YAcXOnWyH4npSszDAEMJ2FKSFPAUyjMg6z3YYrufabuCTiUB5BtuRENJqlEQRu9GlpCBz8WLorlxhOxSXNT+/gxdrdEETZNEQmA1sh+GaylKBz+KAnFNsR0JIq1ASReyi6ti/yF62HKaCQrZDcWndLtO0kKuJq1SxHYJr05QBX80GrvzAdiSEtBglUaTNFLt2IXf1aliq6PLltuIkpqOXMZDtMEgzefA9MDrzLNthuD6zHtizCjj8BtuRENIilESRVmMsFhS//gaKNm4CzHRlkr0sKKBSB65irGc0RCYd22G4j8OvV9eTMtH0KHENlESRVrFoNMh7+BGUf/UV26G4nZ5XaXrIVcSpNWyH4H6ufA98PQfQ0NQ2cX6URJEWMxaXIPuOFag6cIDtUNwS73oqYky+bIdBmiDmiTCOrspzjJwT1fWk5GlsR0LILVESRVpEl5SErMWLoUtIYDsU92WxYHFRFNtRkCaM8oqFB93GxHHKM4D/iwOy/mU7EkIaRUkUabbKw4err8ArKmI7FLfX9xp9ODu7OC2t23E4rQL45nbgEtWdI86JkijSLOVff4O8NWth0dAakPbAv5KCCJM322GQRgi4AkzIpKm8dmE2AD8/CBzYBDAM29EQYoOSKHJLjNmMolc2o/i11+gKvPZkNmNJaTTbUZBGDPeKhadOyXYYHcuxt6pvFWOkqyGJ86AkijSKMZmQ//g6KHbsYDuUDmlAAn1YOKt4vYXtEDqm63uAb+cDBjXbkRACgJIo0oibCVTlvn1sh9JhCS8mI8gsYzsMUgefw8ekrAtsh9FxZf8L7FhIiRRxCpREkXqsCdQ//7AdSsdmNGKJPIbtKEgdg71j4EM1jNiVfZwSKeIUKIkiNiiBci5DEk1sh0DqiDfSadMpUCJFnACdDYgVJVDOR3IhGb4WCdthkBu4HC4mZ19mOwxyEyVShGWURBEAlEA5K0avx+LyLmyHQW4Y4BWDgMpitsMgtVEiRVhESRShBMrJDU+m2jjOIs4sZDsE0hBKpAhLKInq4CiBcn6ycymQMfThzTYOOIjPvcZ2GKQxlEgRFlAS1YFRAuUaGI0GC8u7sh1Gh9fHKxohFXlsh0FuhRIp0s4oieqgKIFyLaNTeWyH0OHFMbTA3yVQIkXaESVRHRAlUK7H+1wKxAyf7TA6tPj8RLZDIM1FiRRpJ5REdTCUQLkmprIK85U0pceW7p6dEVmWxXYYpCUokSLtgJKoDqbwhRcogXJR49NocTlb4jiebIdAWiP7OPD9HYCZitYSx6AkqgMp/fBDKH/5le0wSCv5nk2DkKG1UWyYUpjKdgiktdIPAn8+yXYUxE1REtVBKH/7HfL3P2A7DNIGTIUScyqp8GZ7i5FFIKaEkiiXdv4L4MT7bEdB3BCtVO0ANBcuoPCFF9gOg9jBxHQJfhjYPsdSJ6sh/1MObbYWpgoTOj3cCV6DvazPMwyDkp9LoDiigFlthiRGgrA7wyAOF9+yX+VZJUp+KoGhxABhkBDB84Nt+q04UYGiH4vA6Bn4jvVFyJIQ63OGUgOytmUhdkMseJL2GZWL4/m2y3GIg/3zMuAXA/SYwXYkxI3QSJSbM+TkIG/NWjAGA9uhEDsIOpcBHjjtciyL3gJxJzFC7wht8Hn5n3KU/V2G0DtCEbs+FgJvAbLezIJZa260T02aBrnbc+EzygddNnWBzygf5HyUA026BgBgqjQh/4t8hC4ORecnOkNxXIHKS5XW/Qu+LkDwwuB2S6AAIL44s92ORRyIsQC7VwEFF9mOhLgRSqLcmFmlQu4Dq2FWKNgOhdiJRV6OGVXtM6Xn2c8TwfOD4T3Eu95zDMOgbF8ZAmcFwnuIN8QRYoTfFw6L3gLlKWWjfcr3ySHrLUPgzECIwkQInBkIWU8ZyvaVAageaeJJePAe7g2PGA9Ie0qhK9ABACpOVoDD5zQYj6NEeoSgR2FCux2POJhRA+xcAijz2Y6EuAlKotwUYzQi7+FHYMikb9HuJj5TxnYIMJYaYVKaIOtTEwtXwIW0hxSaNE2j+2nTtDb7AICsr8y6jyhYBIvBUj2FWGWCNlMLcaQYpioTSn4qaXRUzFHihIHtejzSDqqKgJ2LAX0V25EQN0BrotxU4foN0Jw+zXYYxAFCzmaD0wdg2mdWr0EmZfUl43wv21MI34sPY5nxlvs1tM/N/nhSHiLui0Dep3lgDAx8RvnAs68n8v4vD35xfjDKjch5NweMmUHQ3CB4D3XsqFR8aa5D+ycsKb4K/Hg3sPQ7gEtXvJLWoyTKDck//gTKPXvYDoM4CFNcginabvjbI4PtUFBveRbT9n28BnvZLDSvSqyCPk+PsDvCkPJMCiJXR4LvzUf6pnRIu0vrJWX2EiIJRJ/MCw7pmziB1H3A3meB6W+yHQlxYTSd52ZUe/ei9J132A6DONhtWT6sHp/vXZ243BxBuslUabI+19h+LdnHYrSg8JtChN0VBkOJAYyZgbSHFKJQEUQhIuuCdEeIE4eC06yskLisM58Apz9mOwriwiiJciPay5dR8MyzAEMnfncXcT6P1eMLAgXge/NRdb1mXYnFZIE6SQ2PLh6N7ifpIrHZBwCqrlU1uk/pr6WQ9ZVBEiUBY2EAS81zjMn2sb3FyQsd1zlxHnufA1L+ZjsK4qIoiXITxvx85K5ZC0avZzsU0g6YvAKM13Z26DHMOjO02Vpos7UAAIPcAG22FoYyAzgcDvyn+KP0t1Kozqugy9Mh/7N8cEVceI+oWaeU90kein4osj4OiA9A1bUqlP5RCn2BHqV/lKIqoQr+U/zrHV+Xr4PyjBLB84IBAKJQEcAByo+Uo/JSJfSFekhiJA557QEiPwzMpUvhOwTGDPx4D1B0le1IiAuiNVFuwFxVhdzVq2GWy9kOhbSjmbn+ONIt22H9azO1yNqSZX1ctKs6GfIZ7YOI+yIQMD0AFoMFBV8XVBfbjJUg6skomxpOhjKDzRooj64eiHwwEsW7i1GypwTCICEiH4yER6ztSBTDMCj4ogAhS0PAFVV/1+MKuQhfFY7CbwrBGBmErgiFwFfgkNc+2SMcXOaSQ/omTshQVX3F3qoDgFf7XgFKXBuHYWjux9Xlrl2Lqv0H2A6DtLeoSCxaSlNOjvApQjAi8wzbYZD2FjkcWPknwKPxBdI8NJ3n4sq//poSqI4qKxfD9eFsR+F2fITeGJJNV+V1SLmngYOb2I6CuBBKolyY9uo1lLy5je0wCIvm5gazHYLbmSjtBL7F1HRD4p6Ovwek7GM7CuIiKIlyUebKSuQ//jgYY+OFDYn7i71UynYIbidOWc52CIRVDPDzakBVwHYgxAVQEuWiCl94EcY8di9zJ04gNRP9DDQaZS+eAhlGZp1nOwzCNk0Z8OO9gKXxm2kTAlAS5ZLKv92Byn003EyqLcindVH2Ml4WBYHZwHYYxBnknAAObmY7CuLkKIlyMVUpmSjZupXtMIgT6X7Z/tNPpioTEh9OhKHU+RKKnA9yIN/rmHIecZWVDumXuKh//wNN+km2oyBOjK7jdCFGgxm/7ChGwPzXEPHzBnC1dBdyAnCS0tHDGIwkgf0Si9LfS+E5wBPCQCEAoHBHIdQpaujz9RCFitDllS719tHl6lDwbQG0GVrwpDz4TfRD4OxAcDg1haLUSWoU7iqEPl8Pvi8fgdMC4TfJz6Yf5VklSn4qgaHEAGGQEMHzg23upRc0JwiZb2TCd7yvTU2qtpLwJRiddc5u/RHXxnD5+Dd8FZ76rhJ/PKqHv0zEdkjECdFIlAs5/mMaKoo1SCuU4PL0t2DoOYLtkIgzYBgsLOxkt+4sBgsURxXwG1+T3DAMA9+xvvAe5t3gPmatGVlvZkHgI0Ds+liE3hEK+V9ylO0ts7YxlBqQ9XYWpN2kiN0Ui8CZgSjcUQjlWaW1jSZNg9ztufAZ5YMum7rAZ5QPcj7KsblHnjhSDEGAABUnK+z2mgFgrGcMxEatXfskrsnkFYnnvLdgReo4FFUa8cxuqmZOGkZJlIvIvCLH9aP51seKMhNORtyFstmPsxgVcRa9riqbbtRMlVcqweFxbO5nF3ZHGPzj/CEIbLhCeMXJCliMFoSvCoc4QgzvId4InBkI+d9y3KznW36oHEJ/IUKXh0IcJobfeD/4jPWxmZqT75ND1luGwJmBEIWJEDgzELKeMpTtK7M5ntdALyhP2e81A0C82nE3MyauoyD8NoxVvYLvCmsql+9PLMbO0zksRkWcFSVRLkCjMuDQN4n1tpuNFlxWdUHqsg9g8QthITLiLHjXUhFl8rFLX5pkDSRRLbsnnTZNC2kPKbiCmlOKrK8MpgoTjPLqMhyaNA1kfWQ2+3n29YQ2S1t9M+Eb/dRtI+srgybNNsGRxEigzdDCYrTPHYhFPBHG0VV5HRoj8MAPYU9jVPqdKNQJ6z3/yu8JyCilJRTEFiVRLuDAV4nQVjZeDyq3gIOzYzZCO2xGO0ZFnIrFgiVF0XbpyiA3gO/TsuWSRqURfC/bfW4+NilN1r95Xrz6bczVC9lvtmmon5t93CTwFYAxMfW2t9Yor1h46OkDsqPS+vfG3YI38VTGgMbbGM147PtLMJrtk7gT90BJlJO7ejgPOdfLmmynVplwSjodRYs2gKH7PnVI/a6r7dIPY2RsRpSajdOMJhzbRgwauHVn3X4aaiKobmQx2OcDLV7rfFchkvZxPXIphhQ/i8Plvk22vZKnxDv7U9ohKuIqKIlyYooiNU7sTmt2e4YBEkoCcX3BBzB26u7AyIgz4l9JQbjZq+mGTeDJeDBrWlZkUOAtqDcqZFJVP+Z7861/121jVpkBHsCXNd7GVGmy9mHdT10dH9+z7V8Y+Fw+xtNUXodjkfjj3aDNmJE6C2pT86/y3H44HWezqKo9qUZJlBM7vCMZplas+SgpNuN078dRGbfS/kER52UyYUlJTJu7kXSWQJ+vb9k+XSRQJ6thMdX8vlZdqwLfhw9BQPVidI8uHqi6bjtlVnWtCpIoCTh8jrWfhtrUXuQOALo8Hfh+fLskUcO9usBLa99F6sS5VYSMxAzjFvwnp+XvFwsDPLfnKk3rEQCURDmtpJOFKEitaPX+Bq0ZZ01Dkb30LVgksqZ36ADOaTR4KC8X49PS0Cs5CfvrFFZkGAYfyEsxPi0NA1OScVdONlL1TScT+ypVmJmZgf4pyZiZmVGv399USkxKT8OI1BS8WVJi81y+0YBpGemoMtvn9hIDEnRt7kPWRwZdgc462gMA+mI9tNlamJQmWIwWaLO10GZrrUmTzwgfcPlc5H+WD12eDqrzKpT+XoqAqQHWKTy/iX4wyA0o3FUIXYEOiqMKKI4qEHBbgPU4AfEBqLpWhdI/SqEv0KP0j1JUJVTBf4q/TYyaFA1kve3zez1Fb58Pw6PZJszapUHYW5XgbFTh5yTbdYwMw2DDYR3C3qqE5FUVJnypxvWSpv/fdycY0evDKog2q9Drwyr8lGjb744rRkT+pxJ+W1R4ap/t/39WhQXd3q+CSt/AnGgHxHD5OBz5EAZnr0FilUfTOzQiraQKnxzNsGNkxFVREuWEdGojTuxp/jTeraQXinFp+tvQ9x5ll/5cmcZiQXeRGC8GN3yvuf8rL8dXCgVeDA7G/zpHIYDPx6rcXKhvcf+sS1otnigowGwvb/zUOQqzvbyxriAfl7XV9YYUJhNeLirCU4FB+DQiEr+olDhSVTPSsrG4GOsCgyDj2adopOhiMoLMbUsuxJFiSKIkUJ6pGZ3J/zwf6evToTisgKHIgPT16Uhfnw6TonrqjefBQ9RTUTCWG5G+IR0FXxcgYGoA/G+rSX6EgUJErYuCOkmN9JfTUfJrCUKXh8J7aE3tKY+uHoh8MBKKYwqkvZSGin8rEPlgJDxiaz7wLAYLVBdUNnWsWovH4WFi9sU29wMAagOD/sFcfDBd3ODzW48b8PZJAz6YLsbZ+6QIkXEQ/40GlbdIcE7mmrD4Ry1W9BPg8mopVvQTYNGPWpzOq/65yzUWrPpNi23xYvx9hxRfXTbij5SaJOvBP7R4I04EL1EzFqy5OZNXJzzttRUrU8fAzLT9o++Dg2nILaeyGB0drUB2Qqd+Tr/l1XgtVVFmxKmwFejTZQT8f3nbbv26mnEyGcbJGk4wGIbB14pyPODnj3hPTwDA6yGhGJueht9VKiz2aXjR6deKcoyUSnG/f3WycL9IhHMaDb5RlKO/JBy5RiNkXC6meVWvVRrm4YE0gx7jIcPvKiUEHI71eHZhNGJxWQzeD7rSpm6CZgeh6Psi+I73BYfLQcxzTU97iCPFiHn+1u2kPaTosrF+tfPavId62yRWdSmOKiCJkdSb4muNIV6x8M3Y3+Z+AGBaVwGmdb1ZR8u2aCfDMHjntAEvjBVhXs/qNl/NlSB4WyV2XjXigSH1L6kHgHdOGxAfy8NzY6urZT83locj2Sa8c9qAXRF8ZCgYeIs4WNynus+J0TwklFowoxuw86oRQh7HeryOLC9iOhbkLkKRvuGfc2tojWZs/O06PrtrqN36JK6HRqKcTFGmEtf/LbB7v2ajBZeVsUhZ9iHM/qFN79DB5BmNkJvNGCWVWrcJuVwM8fDAJW3jVawvabUY7SG12TZaKsXFG/t0FgqhYxgk6HSoMJtxTadDd5EIFWYz3pfL8WJQw6NibTEkse2X/Xv294TvBF8YFfZL5u2Fw+cg7I4wu/QVZ2qfU2BmBYOiKgZTYmu+t4r4HIyP4uNEXuMjnSdzzZgSY/tdd2osHydyq/fp6seFxsjgYqEZ5VoGZ/PN6BfMQ7mWwcuHdPhgWsOjYh0FI5Di+7BnMSbtDrsmUDftTyzBvutFdu+XuA4aiXIiFguDIzuTG7yk217yCgDF6A3oW/I7PE795rgDuRi5uTrxCODbTqsF8HgoMDaelMhNJvjX2cefz4P8xhonbx4Pr4eE4rnCQugYC2Z7eWGMVIYXCgtxh68v8o1GrMnPg4lhsCYgAFM92351nceFFHiPFUPJbdv6qIApAU03YoHfhLZP4wEABxxMzmnbiF1zFVVVr7sKltlOqwVLOchWNr4mq6iKQbDMNtELlnFRVFV9kvCVcPDVXAnu/FkLrZHBnf0FmNqFj3t+0eLhYUJkVlgw+zsNjGZgwwQRFvTqOKNSWv8+eED7EI5m+Dj0OBt/S8CYrgHwENLHaUdE/+tO5OqhPMhzHV/wT60y4bTkNvRcNBRBezaDa6IaOTdxULeOUdPlj5raJ87TE3G1puzOaNRINejxYnAwbsvIwLawMATweVicnY0hEg/489v2tmR0OiwpH4iPA+h+X7cywCsGgZmH2vWY9UpgMc35/br1Prf3FOD2WlN2h7NMuFpixgfTxejyXhV2zZcgRMbBsM/UGNeZhyCpe09AMODgWuQyLMmYBrXZ8a81v0KLdw+k4rlpPR1+LOJ83Pvd5ELUFXqc/q39rvaorinlj4T578HUqUe7HddZBdwoUFpqsh11KjOb64002ezH50NeZ59ykxn+jSwUN1gs2FRcjA3BIcgxGGAGg6EeHogWihAlFOKKzj43wB2RbJdu3Fq8RdRuxwq5MZp0cwTpphJN/ZEm2/041lEs6z5qS70RrZv0JgYP/aHDxzMlSCu3wGQBxkfx0T2Ah27+XJy+xdShO7BIAvBO0CuYlTqjXRKomz7/NxMpxZVNNyRuh5IoJ/HvD6kw6tr/BFdSbMapXo9BFX93ux/bmUQIBAjg8XBSXVP128AwOKfRYICk8fvIDZBIcEJjWyn8uEaNgY3ss72sDGOlUvQSi2EGYGJqPlSNDAOznaZyZeeSIWPsvwbEncTlXW+3Y0X7cBAi4+CfjJqE22BmcCTLhFERjSfpIyN5+CfD9rywL8OEUZEN7/PKUT2mdeFjUCgPZgtgstT6/TLDbr9fzkgRMhrTjW/g3VbUfmoro5nBiz9da/fjEvZREuUEchPKkXa+pOmGDmLQmXHOOATZy96CRdr2NTnOSm2xIFGnQ6Kueq1QvtGIRJ0OBUYjOBwO7vT1wyflZdhfWYlUvR4vFBZCzOFiplfNz+TZwgK8XVrzf7XC1xcn1Gp8VlaGDL0en5WV4ZRajRW+9dftpOr1+KtShYcDAgEAMUIhuBwOdldU4EhVFTINBvQV22chMKPRYKGiq136ckd9vKIRqsi1a59VBgaXisy4VFSd9GQqLLhUZEaO0gIOh4PHhgvx2jE9fko04lqJGSt/1sJDwMGyvjVTcXf+pMVz+2vWsj06XIh96SZs+VePJLkZW/7VY3+GGY8Nr58gXy8x4/vrJmyaWD3C1iOACy6Hg/+7YMAfKUYkyS0YGmafUhrOhOEKcCjyIQzKfghJbaj91FZnssrx4/k81o5P2EFrolhmNlpw5DvnmHtJLxBDPnUb+qR8DdG1f9kOx+6u67RYmVvzwbnlRjI018sLr4WG4V4/P+gYCzYVF0FlsaCfWIzPIiMh5dZ88BQajTbfPAZKPLAtLAzvyeV4T16KTkIh3goLR/86I1EMw2BDURGeDQqGB7e6BzGXi9dCQvFKcREMDIMXg4IRLLDfwt/RqTx8Mdxu3bmVOMb+H7bnCsyY+FVN3aB1+/QA9LirvwBfzpXg6dFCaE0MHvpTB4WWwfAIHvat8IBnrRpOOUoLuJya37BRkXx8t0CCFw/q8dIhPWL9uPh+gQTDI2xP3QzD4P7fdfjPVBGkwhvV3wUcfDlXjDV/6qA3AR9MFyPcy72+Nxu9OuM57mP4MdX+V7m2xut/JiKuZxB8PGgUuKPgMAzjxgO8zu/M75k4+3sm22HY4PE56CPNgP8v29gOhbQBx9MTd6wxQM9x73UwrfFHJQ+d5M71viMtkxsxA/NzF6FE71xXHC4d1gmvz+vLdhiknbjX1xIXU1GiwYW92WyHUY/ZxOCyMrq6plRAONvhkFZiKisxX9WN7TCcTnfPzpRAuTBGKMXOsOcwNm250yVQAPDd2RxcyFGwHQZpJ5REsejodykwm5z3JpZ5BcDZkS9DM2ou26GQVhqfRtMKdcVx7FghnrQrTUBfrOBvw/MZzjvSwzDAiz9dg9lCkzwdASVRLMm6IkduQjnbYTRJU2nCaVE8Che9AgufPpBdjd/ZdPDtcJ8wdxJfaJ/7UpL2w4CDK5F3YGjhM/i3vPFbAjmLhEIVvj9r3wsXiHOisysLGIZp15pQbcUwQGKJH67Pfw+mzr3YDoe0AKOowNxKukrvpmhpOGJLUtgOg7SAxSMQ2wJfxezU6e1a+6mtPjiYCoMTzzQQ+3Cd30g3knlJ3i6Vye2ttNiMUz0fgWrKvWyHQlpgUkbjda46mji+fW4ZQ9pHecgY3KZ/Ax/mRrEdSosVKHX47mwO22EQB6Mkqp0xDIMzv7vOKFRdBp0Z5wyDkLXsbVhkzj+sflOF2YwxaanINzrfLW4ey8/Hl+WOm9oNOpsJDi3PAADEFztmQXmZxoKgNyuRVeF8Iw8L/qfB2yf1bIfRIgxXgAORazE4+0GkqF33S8CHh9KgM9LVse6Mkqh2lna+BGX56qYbOrmMAhEuTnkT+r5j2Q6lWT4pK8MEmQzhgup1Xa8VF2NBVib6pyTj9qyGP1hT9DrcmZONgSnJmJCeho/kctStCHJWo8GCrEwMSEnGlIx0fFdR/6qcfZUqzMzMQP+UZMzMzMD+StvbQzzo74+Py+SoMjvmZGuRl2GGuotD+nYlER4h6FmY4JC+X//XgFnd+IjyqT6lPvqXDoM/qYJoswoD/tvwqPPVYjPGf6mG5FUVwt+uxKYj+nq/X0eyTBj8SRXEm1WIebcS/z1X/0vA7gQjen1YfaxeH1bhp0SjzfMvjxfh1WMGqPSukUkbvaPxhOebuDd1FBimqTsLOrdilR47TtNolDujJKodMRYGZ//IYjsMu1GWG3EqeCnkc59iO5Rb0lks2KOswAJvH+s2BsA8bx9M82z4Sq0qsxn35uYiiM/H/zpH4YWgYHyhKMeXipoRozyDAavzcjHYwwO7O0fhfj9/vFZcjH2VKmubS1otnigowGwvb/zUOQqzvbyxriAfl7U198jrLhYjXCDA7yoVHGVKJl2RFi8MdEi/WiOD/7towKpBNZfbMwDuGSDE4t4NX4Kv0jOI/0aDME8Ozt4nxfvTxNh2Qo+3T9YkSZkKC6bv1GBsJz4uPiDF82NFeOQvHXYn1CRJJ3NNWPyjFiv6CXB5tRQr+gmw6EctTufV3F6mXzAPUT4c7Lhim1w5o+yI2Ril2IA9xUFsh2I32w+nQ2ug0Sh3RUlUO0o5WwxFoeuPQtVmNjG4UhGF5GUfwhwYwXY4DTqmVoPH4djcA++F4GAs8/VFRCMVwn9XqWBgGLwWEoquIhHiPT1xv58/vlIorKMF3ysrECoQ4LmgYMSKRFjg44N53j74otbU3NeKcoyUSnG/vz9iRCLc7++PER5SfKOwnb6bKPPEH5WOS6JCzzlfPbL2FlfqmFty/JVmAp/LwcjImiri700TY80wIWJ8Gx5J2XHFCJ2JwZdzJOgTxMO8ngI8P1aEt08ZrL9f/z1nQCdvLt65TYyegTysGiTEPQMF2FYr0XrntAHxsTw8N1aEHgHVf0+O5uGd07YjVrO7CbDrmvMmUYxQhh1hz2N82hKUGpyv9lNbyKv0+PpkFtthEAehJKqdWCwMzv2ZxXYYDpNfAJwd8ZJT1pQ6p9WgdwvvSXdJp8UQDw8IuTVvkdFSKUpMJuQbqz+MLmm1GOUhtdlvjFSK6zodjDc+CC9ptRhdp81oqRQXa41EAUBfsRhXdToYLI5ZU8MUlWCKuv1vzOosgiUB6Jt3xSF9H802Y0hYy06lJ/PMGB/Fh4hfk2RNjeWjoJJBVgVjbTMlxvb2LlNj+ThXYIbxxp2ET+Y23OZEru3Ix7BwLs7km6E3Od+UniagH+7gvYkXMvqwHYrDfHw0A2q9qemGxOVQEtVOkk8VoaJY03RDF3azplTB4s1OVVMq32hEEL9lt4mUm0wI4NnerDWAX/1YfmPtktxkgj/fto0/nwcTqhey36qNvM76p2A+HwaGQamD1kUBwG3ZPg7r29nFiULBgWMSiKwKC8JkLTuVFlVZECy1HaUKlnGsz1X/zVi31W5jsgByDVOrDbdOGy6Kqmxfa7gXF3oz6m1nEwMOLkXeicGFT+G4wnUuUmmNcrUBX57IYjsM4gCURLUDi9mCc392jNtMMAyQVOyL6/PehzHaOb5Z6i0MRJzWLFC13efmml+OTYuG2+BWber1DIhujHjpHDQSBQCRFzruHebjy4oc1rfWxEDcilu51/0dsP5+cdrepu42yY34NEbnSKIsHoHYGvgq5qbeBq2Z1/QObuDTYxmo1DnvlCppHUqi2kHSySKo5Dq2w2hXpSUmnO6+Fsqp97EdCnx4PKjMLUtOAvh8yM22w+9lN0aJ/G+MUAXw+ZCbbNuUm83g3zhmo21MZmsfNylv9O3Hc9wHCpNbgLG6Tg7r31n5i3wxMPeiw/oP8OBAoWtZchIi46JIbbtPyY3HwVLujTaceiNHJWoGfC7gL+HUamOp08ZSbwSrXFvdT6CU/avdykLHYar+DWx3wdpPbVGhMeLzf7PYDoPYGSVRDmY2Wdx6LdStGHRmnNcPQNay/8Ai82Etjp5iEdIMLauTM0AswXmNBoZaQ0sn1GoE8fkIv7EYfYBEghMa2wsFjqvV6C0WQ3BjqKDBNho1Bkpsa9+k6fUI4fPh28Jpx5aalRPg0P6d0WSPCHAZx43wDQzhIaG0Zf2PjODhaLYJBnPN79e+dBPCPDmI8uFY2/yTYZuA70s3YUgYDwLejTaRPPyTYTsFvC/DhFGRtsn4tRILIrw4CPBg75TP8IT4J+IRDMl6AKkuXPupLT77NwNKLY1GuRNKohws8XgBKss71ihUXRkFQlycshX6fuNYOf4YqRTper11tAcAsg0GJOp0kJvM0FsYJOp0SNTprEnTDC8vCDgcvFBYiFS9HvsrK/FJeRnu8vUF50aCtNjbB4VGI7aUFCNdr8duZQV2Kytwt19NVewVvr44oVbjs7IyZOj1+KysDKfUaqzwta2cfV6rwSip7QJ0R4i+4LhpLWcVpyh1aP9TY/m4XmqBQluTEKWVW3CpyIyiKgZaE3CpyIxLRWZr0rSsrwAiHgcrf9biWokZPyUa8dq/eqwbIbT+fq0eIkS20oJ1f+uQWGrG5xcN+L+LRjw5sma94aPDhdiXbsKWf/VIkpux5V899meY8dhw2zWJx3LqL0BvT0bvGDwu24r70ka4fO2ntqjUmfDZMdcttkzq4zB1q7sRuzEbLfjmpZNQV7hWtWBH4fI46OOVDf+ft4LTzr92S7OzMNfbG4t9fAEAd+Vk42ydK+QA4J+YGGtBzhS9Dq8UF+OqTgcvLheLfXzxkL+/9UMOqC62+UZJMdIMBgTx+bjXzw9Lbhzjpr8rVXhPLkeuwYBOQiEeDQhEfK36VHqLBWPT0/BpRCT6Sxz/DX3bus44I8p3+HGcgbfQC4dTk8C3OPbKqJH/p8bK/gI8MKT6d2fCl2ocya5/kUDmozJrQc6rxWas+VOHM/lm+Eo4WD1YiJfHC21+v45kmfD43zpcL7UgzJODZ0aLsHqIbYL0Y4IRLx7UI0NhQawfF69OEmFez5oyAToTg+Btlfj7Dg+MiGj/RCorYg4W5sxzu9IFrSUT8XHs6YnwlTrPxTek9SiJcqDLB3Px7/9S2Q7D6YSHctDlwBvglbRfJd8jVVV4s7QEv0ZFg9uqReaOs1OhwMGqSnwW2T7rldIWDcPzsRfa5Vhsm+vbF69c+MPhx/kz1Ygn9+lx7SGp0/1+fXjGgF+Sjdi3wvEjnbUxQhm+8X8ML2fSTcvrWj0+Fs9O68F2GMQOaDrPQSxmCy7uo3L/DckvZHB22AvQjJ7XbsccL5NhsY8Pik3OV6uFz+HgheDgdjtel0uOnd5yJvHK+rfhcYTpXQV4YLAA+Srn+04q4AHvT2tZnbS2UgcOwBLuNkqgGvH1ySyo6Eo9t0AjUQ6Sdr4Ef396je0wnBsH6BFUgZCfNoHbwoXfpG1eeSIMV4UlbIfhUJ4CGY6kp0Fgdr6bTrsrBhxcjLwTyzPiOkzpgtZaP6sX7h4dzXYYpI1oJMpBrh3tuDV5mo0Bkop9cG3uezDG9GM7mg5lQb5z3qLHnsbJoiiBakdmaRDeCHgd81KnUgLVDN+eolsxuQNKohxAUaRGfnIF22G4DHmJCae6PgjlbfezHUqH0f1KedONXFx8ZSXbIXQYZaHjMUX7Oj7O63h1yForvVSNE2lytsMgbURJlANcPdIxrnyyJ6PegvO6/shkuaZUR8FNTEd3o/vWjJLwJRiddY7tMNwewxNib8SjGJJ1P9I1HbP2U1t8Q6NRLo+SKDsz6s1IPtXxavHYS2aBEBfit0LffwLbobg3hsGiIvcdNRjrGQOxsX4JC2I/Bp8YPCJ9E6vThnfo2k9t8U9CMYpVHbuOoKujJMrOUs4UwaB1vivAXIlKYcTJgEUonfcsGCe7XNyd9LqqZDsEh4lXUwLlSJkRczGyfD1+KwlkOxSXZrIw2HmaruJ2ZZRE2RlN5dmHxczgankkkpd8CFNwZ7bDcUu8q6mIMvmwHYbdiXgijKOpPIdgRJ74IvQlTExbhDIqnmkX353NgamF9/YkzoOSKDsqTFeiLK+K7TDcSkEhg7NDn4d67AK2Q3E/FgsWF0exHYXdjfSMhYee3of2pg4cgMWcN7ExsyfbobiVYpUe+xKK2Q6DtBIlUXZ07QiVNXAEbZUJp/kTkb/4NViEIrbDcSv9rmvYDsHu4qmIoV0xHC7OR96NQflP4kyFF9vhuKVvTtICc1dFSZSdaCsNSL/QcSpBtzsGSC72xrW578EQSzWl7EVwOQWhZs+mG7oIPpePCTSVZzdmaQhe9X8d81PjobfQx4WjnMwoQ1oJjZ66InpX2EnC8QKYTTSv7WjyEhNOd3kQymmr2Q7FPZhMWFoay3YUdjPcqwu8tO67YL49ycMmIF77Gj7Li2Q7lA6Bim+6pnZPooqKivDwww8jJiYGIpEIkZGRmDVrFg4cOGC3Y0RFReGdd96xW39NYSwMrh8raLfjdXRGvQXntX2RuewdWDz92A7H5Q1McJ9b7sQb6ItMWzE8Ef6KeAxDMu5HhqZ977nXke2+kAeNwfFXdnM4nFv+WblypcNjcCf89jxYVlYWRo8eDR8fH2zduhX9+vWD0WjE33//jTVr1iApKak9w7Gb7GtlqCyjWh/tLbNAgLK4N9An8zuILx1kOxyXJbqQhIAJUsi5arZDaRMeh4dJWRfZDsOlGXy64DHzI/gzzX0LsTqrSp0JP18swLLhjq3fVlhYaP33999/j5dffhnJycnWbRIJFU1tiXYdiXrooYfA4XBw5swZLFiwAN26dUPv3r2xbt06nDp1CgCQk5ODOXPmQCaTwcvLC4sWLUJxcc2VC+np6ZgzZw6Cg4Mhk8kwdOhQ7N+/3/r8hAkTkJ2djccff9yaWTvaVVpQzhqVwohT/gtQOu85qinVWkYjlshdf0pvsFcsfNVlbIfhstIj5mF42cv4s5QSKLa0RwXzkJAQ6x9vb29wOBybbTt37kRsbCyEQiG6d++Ob775xmZ/DoeD7du3Y9q0aZBIJIiOjsYPP/zg8LidVbslUeXl5di7dy/WrFkDqVRa73kfHx8wDIO5c+eivLwcR44cwT///IP09HQsXrzY2q6qqgrTp0/H/v37cfHiRUydOhWzZs1CTk51wbI9e/YgIiICmzZtQmFhoU3W7QjKUi1yEtz/PmTOrLqmVASSlnwIU2gU2+G4pKFJZrZDaLM4Ey3xbA1G5IX/C30Zk9MWQGFs18kJUkdioQrns9n7PPnpp5/w6KOP4oknnsC1a9fwwAMP4O6778ahQ4ds2r300kuYP38+Ll++jDvuuANLly5FYmIiS1Gzq93OOmlpaWAYBj169Gi0zf79+3HlyhXs3LkTgwcPxvDhw/HNN9/gyJEjOHv2LACgf//+eOCBB9C3b1907doVmzdvRkxMDH799VcAgJ+fH3g8Hjw9Pa2ZtSOlnCkCGIcegjRTYSGDs4OeQ9W4RWyH4nI8zifD2+K661844CAu5wrbYbicqsBBWIg38Upm4+dl0r52X2CvYPO2bduwcuVKPPTQQ+jWrRvWrVuHefPmYdu2bTbtFi5ciFWrVqFbt2545ZVXMGTIELz//vssRc2udkuiGKY607jV9FpiYiIiIyMRGVlzNUivXr3g4+NjzXLVajWefvpp63aZTIakpCTrSFR7Sz1XwspxScO0ahPO8MYjf8nrYISumxS0N0anw+LyLmyH0WoDvGMRqKJ7VjYXw+HibOQ9GJy/DueU7lPiwh3svVbEWgXzxMREjB492mbb6NGj640yjRw5st5jGolysK5du4LD4dzyB80wTINJVu3tTz31FHbv3o1XX30Vx44dw6VLl9C3b18YDAaHxd6YsvwqKApdezGuW2KA5CIvXJnzLoxdBrIdjcsYkeK6a8rizEK2Q3AZZlkoXvF7HQtT46j2kxMqVxtwIp29tX11P4Mb+1xuar+Oot3eQX5+fpg6dSo+/PBDqNX1E4+Kigr06tULOTk5yM3NtW5PSEiAUqlEz57Vtxo4duwYVq5cidtvvx19+/ZFSEgIsrKybPoSCoUwmx2/xiPtPI1CObOyUhNOxtyPiukPsR2KS/A8lwIZ45rJSFxeAtshuISSsMmYrH4Vn+dT7Sdn9vsVdkrm9OzZE//++6/NthMnTlg/f2+6eSFY7ce3Wqrjztr1a8hHH30Es9mMYcOGYffu3UhNTUViYiLee+89jBw5EnFxcejXrx+WL1+OCxcu4MyZM7jzzjsxfvx4DBkyBADQpUsX7NmzB5cuXcLly5exbNkyWCy2Q59RUVE4evQo8vPzIZfLHfZ6Us/R/Y6cnclgwQVNb2QsexcWL3+2w3FqjFqNBRVd2Q6jxXp7RSNMwc50vqtg+GL8HvE4hmXciywtTXM7u7+vF8PIwpTeU089hS+//BL//e9/kZqairfffht79uzBk08+adPuhx9+wOeff46UlBSsX78eZ86cwdq1a9s9XmfQrklUdHQ0Lly4gIkTJ+KJJ55Anz59EB8fjwMHDmD79u3gcDj4+eef4evri3HjxiEuLg4xMTH4/vvvrX385z//ga+vL0aNGoVZs2Zh6tSpGDRokM1xNm3ahKysLMTGxiIwMNAhr6U0pxLKEq1D+ib2l1XAx/lJr0M3KI7tUJzamFQe2yG0WBzjwXYITs3g2xUPSbZibdpQtkMhzaTUGnEstf1vIzZ37ly8++67ePPNN9G7d298/PHH+OKLLzBhwgSbdhs3bsR3332Hfv364auvvsKOHTvQq1evdo/XGXCYmyu+SYuc/CkdF/6mMv2uhsvloLdvPgL2vAYO/erXw/H0xB1rDNBzXKfkwR+VPHSSZ7IdhlNKi5yPhVlzqHSBC5o3KBxvLxrAdhj1cDgc/PTTT5g7dy7boTgFWlXYSmnnaSrPFVksDK6WhSFpyUcwh0azHY7TYSorMU/Vje0wmq2brBMlUA1gRN74NGQ94lLnUwLlov65Xgy9yXW+zHRUlES1QmlOJVRyus2LKysstOD0oGehHr+E7VCczvg0EdshNFscz5vtEJxOZdAQzMebeDWrO9uhkDao1JtwPM1xa3qJfVAS1QqZl9t/rprYn05twmnOWOQtfYNqStXifzYNfMY1Tg3xhWlsh+A0GA4XpyNXYVDe47iglLEdDrGDfxKcb8bj5p1FSDXXOFM6mYzL9O3AnaQUeuLKnHdh6Dqo6cYdAKOowJwq579KL1oaji7FyU037ADMsjBs9H0Di1MnwWjpmPV63NE/CSWwWGjtpjOjJKqFVHItyvKq2A6D2FlZqQmnou+DYsYatkNxCpMynP+Ktzi+H9shOIXisDhMVG/GlwURbIdC7ExepcfF3Aq2wyC3QElUC2VeoVEod2UyWHBR3Qvpy96Fxbtj38k+6GwmOE7+BTi+uGMvKGf4YvwasQ7DM+5BDtV+clvOOKVHalAS1UK0Hsr9ZRfwcX7ia9ANimc7FNYwpXJMVzvvvfTCPYLRs7DjVinX+3bHA+JteCRtCNuhEAfbl0D3hHRmlES1gE5tRGGqku0wSDuorDDilM/tKFnwIhiu6xWgtIepmc57Y9p4YTDbIbAmJXIhhslfwD45TWd2BBmlaqSX0hISZ0VJVAtkXyujRX4diMXC4Jo8FEmLP4ApLIbtcNpd6HnnvZVKnDy36UZuxiL2wX+DN2BK6u1QUu2nDoWm9JwXJVEtkJtQznYIhAWFhRacGfgMqiYsZTuUdsUUFiNO43wFSYMlAeiXe4XtMNqVKmgo5lm24o1s1ymESuyHjVvAkOahJKoFClIr2A6BsESnNuEMxiBvyRYwIgnb4bSb6dm+bIdQT5woDBx0jBFhhsPDycj7MDjvMVxSUe2njupCdgUrNyQmTaMkqpkqy3WoLKcq5R1dSpEMl2e9A0O3jrGgN/J8Ptsh1BNXXsh2CO3C5BmO9b5bsDR1ItV+6uC0RjOu5dN6XGdESVQz0SgUualcbsKpqHuhmLmW7VAcjsnNxxhdJNthWPmLfDEo5yLbYThcUXg8JlRuxtcFYWyHQpzEmUxaTuKMKIlqpsK0CrZDIE7EZLDgYlVPpC97z+1rSs3OCWQ7BKtJHhHgMu47rcHwJfg5/EmMSL8beTrXuYchcbyzWZREOSO6xKOZaCSqWlrBFey//D1y5KlQacpw35SN6B89xvo8wzD48/zXOJ74B7T6SnQO6onFYx5BqF/ULfu9mHEUf5z9AnJVIQK8QjFr2L02/Z5N3Y9fTn8Gg0mHkd2n4faRD1ifK6sswgd/PI2n522HRCi1+2u+lewCHsomvIp+ebshPr+vXY/dXqIuFgFOsp45TuG+xW71ft2x1vAw/klv/9IFutxrUJ3eDUNxOsxV5Qi8/QV4dBtpfZ5hGCiP70TV5b9h0VVBGNoNfvEPQhjY+Zb9qpOPQ3nsWxgrCiHwCYXPuBXw6DbK+nzV9UOoOPIVGKMOsn5T4DvxHutzJmUxir9/CaF3vQOuyPkr6Dva2SwFGIYBh0NTu86ERqKaQVtlgKJIw3YYTkFv0iLcPxaLRj/c4PP7L3+HQ1d+xKLRD+OpeR/By8MX7//xNHSGxn9+GUXX8cX+VzC0WzyeXfAJhnaLx//t34Ss4kQAQJVWiZ1H3sLtIx7Amulv4HTKPlzLPmXd//tj72DOsPvaPYG6qUppwinvuShe+JJ71pTKyMFQPfvTSt5CLwzLPs92GA6RHLkIQ0pewD8s1X5iDDoIgmLgF7e6wedVp3dDdfZn+MWtRsidb4Mn9UXJ/16CRd/4+1qfnwj5L1sg7T0RYXe/D2nviSj9ZQv0BdX3OzRrlCjf+z58J96DoEWbUHXtADTpZ637l/39EXzHr6QE6gal1ojk4kq2wyB1UBLVDFRgs0bvTsMxa9g9GBAztt5zDMPg0NU9mDpoGQbEjEWYXzRWTHwGRpMO59IONNrn4at70CNiMKYOXIYQ306YOnAZuocNwqGruwEA8spCiIVSDO4yEZ2DeqBb2AAUKbIBAGdTD4DHFTQYT3uyWBhcLw1B4qIPYQp33krfrTU3L4TtEDBR2hl8i4ntMOzKIvbFR8EbMTV1LipN7E0MSGKHwHfcCnh0H1XvOYZhUHnuF3iPXAyP7qMgDIxCwIx1sBj1UCceabRP1blfIY4aCO+RiyDwj4T3yEUQd+4P1blfAACmiiJwRB6Q9hwHUWg3iDv1g1FeXZtMnXAYHB6/wXg6MloX5XwoiWqGAloP1SxllYVQacrRI6LmyjUBT4guof2RUXy90f0ySxJs9gGAnpFDrPsEeYfDaNIjV54KtU6F7NJkhPnHQK1T4Y9zX2LRmIZHxdhQVGTGmf5PomricrZDsasul9mfRotXKtgOwa5UwcMw17IFW7O7sh3KLZmUxTCrFZBED7Ru4/AFEEf2gT4/sdH99PlJNvsAgCR6kHUfvl84GKO+egpRWwlDYQqEgVEwaytRcWwH/OIbHhXryCiJcj60JqoZaD1U86g01R9ynhLb2kKeEl+UVzVecVelKW9wn8ob/XmIPLFi4jP4+tAWGE16DOsWj16RQ/Ht4Tcxvs9clKmK8PHel2C2mDB9yJ0YGDPezq+sZXQaM85gFLou7Y/wnzaCq1OzGo89cJIz0NcQhqvCElaOLxNIMTL9HCvHtjeGw8PJiHuxIm0czIzzf481V1W/D7kePjbbeVIfmJSN/z6Y1QrwpPX3Maur++OJZQiY8Tjkv78NxmSAtM8kSGIGQ/7nO/AcPBMmZTFKdr8CWEzwHr0M0h5jGjhKx0KLy50PJVFNMOhMkOfRfYtagoO6Cx+ZBrbV2afeYkkGtXfpHz3GZqF5SsElFJRnYtHoh7Hhuztx9+QX4OXhhzd/WoMuof3qJWVsSC2UQj7zP+h97VMIk842vYOTW5AfgavR7CRR42TREJgbH/VwFSbPcKznP44dqeyvMWuxuu9Rhqm/rf5OdXZhbLZ5dBtls9Bcl3MFxtJs+MWvRsEn9yNg1lPgSX1R+PU6iCP71EvKOppilR7ZZWp09mdn/Sepz/m/BrGsKF0Jhu6X1yxeHtWJi0pr+22pUlsBzzrfYm3384NK08A+jSRCRrMB/zv2LpaOfQylqnxYLGZ0DeuPYJ9IBHlHWBekOwOF3IhTne6BYuYjbIfSZt2vsjedFl/p+gtqC8OnYnzlZuwodK0Eiierfh9a1Lb//2aN8pZJDU/qax11uslyi30YkxHl+7bDb+oamBSFYCxmiDv1hcA/AgK/cOgLk9v0OtzFaZrScyqURDWB1kM1n79nKLw8/JCUV3MFlclsRFrhZcQE9250v+igXjb7AEBS3rlG99l7/lv06jQMkYHdYGEssDBm63NmiwkWJ6sjVF1TqjvSlr0Ps18w2+G0GjchDV1N/u1+XAlfgjFZrjuVx/Al2BP+FEam34V8F6z9xPcOBk/qC21WTZFTxmyELvcaROE9G91PFN7DZh8A0GZebHSfihPfQRwzGKKQLgBjASw172vGYgIszvW+ZstZSqKcCiVRTaD1ULb0Ri3y5GnIk6cBqK7RlCdPQ3llMTgcDib2nYd9F3ficua/KCjPxDeHt0LAF2NIl8nWPr4++AZ+Of2Z9fGEvvOQlHcO/1zahSJFDv65tAtJ+Rcwse/8escvLM/ChfTDmDFkJQAg2KcTOBwOTiT9iWvZp1BckYPOQd0d+0NopZwCLs6P3QTtsOlsh9I6DIPFhZ3a/bBjPGMgNmrb/bj2oPPriVWiN7EufWDTjVlkMWhhKM6AoTgDQPVickNxBkyqEnA4HHgOmQPlyR+gSTkBQ2kW5H+8A65ABGnPmvWH8t/fguLIl9bHnoNnQ5d5EcpTP8JYlgvlqR+hy74EryFz6h3fUJoNTdJR+Iy5AwDA94sAOFxUXt4HTfpZGMvyIAx17gX47eUMrYtyKrQm6hbMRgtKslx/GsGeskuT8d5vT1gf7zm5HQAwvNsUrJj4DOL6L4HBZMD3/74Ljb4SUUE9sXbGFoiFNbVeyqtKbNZAxYT0xt1xL+L3s1/g97NfIsArDPdMfglRwbbfWBmGwa6jb2PeqAchElTfBFjIF+GOCU/jf/++B5PZiEWjH4aP1HkqbNdVpTThlGwGei0cgqDdr4JT69u2K+h9tRJo57vAxGtcM4FKilyMhZkzWC1d0FyGolQU73re+lhxsPpLjrTPZATMeBxew+eDMelRvm87zLoqiMK6I2jRJpsaTiZVKcCp+V4ujuiJgNlPo+LYt6g49i34PiEInP0MRGG2X3IYhkH53x/Ad9J94ArFAACuQAT/6Y+h/J/tYMxG+MWvBt/Tve8M0FzZZRqUqHQI8hKzHQoBwGGqV/qRBhSkVuCnty6wHQZxU8EhXHT/923w81LZDqX5uFw89YQfsvkV7XI4IVeIo3nFkOpd58uMReKHDz0fx1s5sWyHQtzU+0sHYlZ/11pb565oOu8WijKoyCZxnOIiC073ewKVk+5gO5Tms1iwpDi63Q43yjvWpRIoZfAIzDFtoQSKONT5bPeqmebKKIm6hfJC16/vQ5ybXmPGWctI5Cx9Exaxa1y23O96+70v4rSuUaGc4fLxb+QDGJSzFlcrXeP/kbiutBIqu+MsKIm6BQUlUaSdpBV64PKMt2HoOZztUJokuJyCULOnw4/D5/Ix0QWuyjN5RuAF7y24I3W8SxTPJK6PkijnQe/4W6goppsOk/ajKDPhVMRKlM9+jO1Qbs1kwpLSGIcfZrhXF3hpnXtKvSD8NoyvfAU7C0PZDoV0IEUqHar0rjFK6+4oiWpElUIPg861rpxytCqdEs9+NR9llUVsh1LPZ/s24MCVH9gOo81MRgsuqbo6fU2pQYkGhx8jzuC817wwAg/8GPY0RqXf6ZK1n2oza1XIfX85TMrGb83EltKfXoPqzE9sh+GU0mk0yik4/7W3LFEU01ReXfsu7kLfziPg7xkCAPjx+AdIL7qGwvIsBPt2wnMLPqm3T35ZBn44/j6yS5LgIfLEmF4zcdugFTYlDlILLmPPye0oVGTB2yMAcQMWY2yvWTb9XMw4ij/OfgG5qhABXqGYNexem9vATBu8Au/+9gRG9ZgOidD116TkFHBRNmYT+hX9AsmZP9kOpx7xhWQEjJdCznXM+4TH4WFSnUKNzkLn3wurtWtwOIP9WwvZg+rkD5DEDgPfuzppL9//MfR5CTDIsyHwj0TY3e/X28dQmoXyf/4LQ2EKuGIZZAOmwXvUEpv3tS7nKhQHP4NBngO+zA9ew+fDc6BtjTR18nEoj30LY0UhBD6h8Bm3wuY2MN6jl6J41/OQ9Z9qU06BAOmlVegf6cN2GB0ejUQ1QlFIU3m1GUx6nEz6CyN71JwEGQAjut+GQbETGtxHa1Djgz+ehreHP56a9xEWjn4YBy7/gIO1RozkqkJs/+t5xIb0xbPzP8bUgUvx4/EPcDHjqLVNRtF1fLH/FQztFo9nF3yCod3i8X/7N9nc3iXcPxb+niE4l3rA7q+dLWpVdU2pooUbwPCc6/sOYzBgsdxxV6AN8oqBn1rusP5bKyFyKQYXP4fD5e6RQFmMelRd2QfP/lNstsv6xUPaY2zD++g1KP7+RfBkfgi58234xa2G6sweVJ6tGTEyVhSh5McNEEX0RtjK9+A1chHK938CdfJxaxt9fiLkv2yBtPdEhN39PqS9J6L0ly3QF9Tc3kUYFA2+dxDUCYft+8LdAK2Lcg6URDWioohGompLyDkDHpeHmJCaW7EsHL0W4/vMhb9Xw+tBzqUegMlswB0Tn0aYXzQGxIzFlIHLcPDKj7hZnuzfhN/gKwvCgtFrEOLbGaN6zsCI7rfhwOX/Wfs5fHUPekQMxtSByxDi2wlTBy5D97BBOHR1t83x+nYehXNpBx3w6tnDWICE0kAkLPwApkjnqsQ+LMlx093xTlag0iLxx7tBmzE9dRbUJh7b4diNLuM8wOXZ3IrFL+4BeA6aCb5PSIP7qBMOgzEZETD9cQgDo+DRfRS8Ry6C6uzP1vd11aW/wPMMhF/c/RAERMKz/1TI+sVBdWaPtR/VuV8hjhoI75GLIPCPhPfIRRB37g/VuV9sjifpMhzqhCMOePWuLb2UkihnQElUI8qLaCSqtrTCK+gU2K1F+2QWJ6BLaH8IeELrtp6RQ6DUlFnXVWUWJ6BnxGCb/XpFDkWOPAVmc/XCycySBPSIGGLTpmfkEGQUX7fZ1jmoO7JLk2A0O369TnsrLjLjdJ/HUTn5TrZDsfK4kAxvxv5VkzngYHLuFbv321oVISMxy/QG/pPj+MX07U2Xew3CkJbdTkWfnwhxZB9w+ALrNkn0IJiryq3rqvT5SZBE297qRhI9CIaiNDA33teNtdHn295AXBTaDfrCFDAmY4vidHc0EuUcKIlqBI1E2SqvKoK3R8tuPqvSlsNTYjvtcfOxSlN+yzYWixlVOqW1bUNtKjW2Bed8pAEwmY31trsLvdaMs+bhyFm6DRaJjO1wwGh1WFRu//uZ9feKQZCy0O79thTD5eNo5IMYnL0G19209pNJVQKezK9F+5jVFeBJfWy2cT2qH1vUihttFOBJfeu08QUsZpi1qlptbPvhSX1gVtu+f3me/oDZWG97R5dTroHJTDdlZhslUQ0waE1QK91vNKMtDCYD+LVGlJqr1jrTG5gb2zmNNmIaaMOp1xED1Nkk4ItuxKprcZyuJK1QgsvT34Kh10i2Q8HI5KbbtFScA0a3WsrkFYlnvbfiztSxbl37iTHqweG3/H1d780HppHt9dtwbNrUee8zTL1tN+NjjPoWR+nOjGYGWWU0Y8I29z07tEE5jULVIxN7Q2No2fCxl8QPqjqjQpXaCgA1I1JeEj9UamzvSl6lrQCXy4NU5FXdxsPPOnJVu5+6o1MaXeWNWH1aFKcrUpSZcDL8TpTPWcdqHJ7nUuBhETTdsAXi8q433ciB8iKmY6zqFXxf2PCaIHfC9fCCRdey93VDo0UWTfWoMffGyBJP6ttAmwqAywNX4nmLNsp6o1M34+N6eLUozo6ApvTYR0lUAypoPVQ9EQFdUKTIbtE+0cG9kFZ4BSZzzVqGpLxz8Pbwt5ZJiA7uhaS88zb7JeadQ6eAbuDduCItOqh+m6S8c4gJ7m2zrUCRCR9pIGQS7xbF6arMRgsuKWORuuwDWPzY+cBn1GosVLZsrdyt9PKMQnh5jt36awlGIMX/wp7BmLQ7UKhrzeiM6xEGxcJY1rKftyi8J/S518DUel9rMy+CJ/OzlkkQhfeALuuSzX7azIsQhnQB58b7WhTeA9o6ZSy0mRdtFrkDgKE0GzzPAPA8Osb7uiVocTn7KIlqgIJGourpGTEEhYosaGrdDLZUmY88eRpUmnIYTXrkydOQJ0+zJk1DukwCnyfAN4e3oqA8E5cz/8XfF3dhUr8F1um5Mb1mobyqBLtPfIQiRTZOJv2Fk0l/YXL/RdbjTOg7D0l55/DPpV0oUuTgn0u7kJR/ARP7zreJMb3war1F6h1BbgEHZ8dshHb4TFaOPybVflerxXPYWeul9e+NlcKteDqjPyvHZ4skZhCM8hyYa41GGRUFMBRnwKxWgDEZYCjOgKE4w5o0SXuNB/gCyP94B4bSLGhSTkB58n/wGjrX+r6WDZgGk6oE5Qc+hVGei6or+1B15R94DZtnPY7n4NnQZV6E8tSPMJblQnnqR+iyL8FryBybGPV51yGOsl2ATqpRwU32cZib16QSqz+3X0HmZeerUcO2bT+txYjuUzHmRiHMd35dh7TCy/XabVy2wzrSlF+Wgf/9+x6yS28U2+w5C9MG1y+2ufvkRygqz4a31B9xA5Y0UGzzCH63FtsMw6yh92BATE0dG6PJgOe+WYA1099AdHAvR7x8p8fhAD0C5QjZ/Qo45va7JQTHywt3PKSHntP2kge/V/LRWZ5hh6ia71rkMizOnOZWpQtaovCbJyDrGwfPAdMAAEU7n4U+91q9duGr/8860mQozUL5vu3QF6aAd7PY5uiljRTbzAZP5g/vhoptJv2LimPfwlRRBL5PCHzH3QmP7jXFNhmTAbnv34HgRZsgCu/hiJfv0vpHeOOXtWOabkgchpKoBuxYf4rum9eA6zmn8dPJ/+L5Rf8HLse5BjGPXPsZV7NPYO2MrWyHwrqgYB66n/wPBDkOWPXdiD0P9cF33klt6qOrrBP2XP3XThE1zSIJwHuej+EdNyxd0BLa9LNQHPocofd+CI6Tva8rL/wOTeppBC9+he1QnJJMxMe1jVPZDqNDc653jBMwmy1QlWrZDsMp9e40HKN7zYTSCStJ87h8LBz9MNthOIWSYjNO934clXEr2+2YE9Lafv+4eF77rXmpCBmFGcY3OnwCBQCS2KGQDbgN5soytkOpj8uHX9wDbEfhtKr0JhQp3ftqZGdHI1F1KEu1+Palk2yHQYhdxIbqEPnrRnDVKoceh+Prg6UPaGDitL5uzR6NBF2LHTt6xnD5OBJ+P+5NG+XWpQtIx/HD6pEYGtWyWl/EfugsUoe2iupDEfeRXijGpdu2Qd97VNON24BRVGB2VZdW7x8lDXd4AmX06oynvbZiZeoYSqCI2yijzyxW0ZmkDl0V3VqAuJeKMiNOha1A2ZwnHHqcyRmtr+odz3fsN+m8iBkYq9yIH4rcv/YT6VgUGkqi2ERJVB2URBF3ZDZacFkZg5RlH8Ls3/ANo9sq6GwmOK1cHBBXkmXXWG5ihFJ8F/YcxqQtR5G+Y9R+Ih1LuZqSKDZRElWHlpIo4sbyCoBzozdAM2JW041biCmVY5omtsX7hXsEo1eB/auUawP64C7+m3g2o6/d+ybEWSgoiWIVJVF16Gh+mbg5tcqE05LbULR4Eyytum9a46ZmtvzWHHHCYLvGwICDq53uwJDCZ3G03MeufRPibGgkil2URNVBI1GkI2AYIKHYH9fnvwdTZ/sVJw07n9vifeLkLd+nMRaPALwdtBmzUqZDbabTG3F/5bQmilV0lqmD1kSRjqS02IxTPR+BKv5uu/THFBRhsjaq2e2DxAHon3vFLsdWhIzGdMMbeD8n2i79EeIKaDqPXZRE1UFJFOloDDozzhmHIHvZW7BIWz4dV9f07OZfaRcnCQMHbStVx3AFOBi5BoOyH0JSlUeb+iLE1dBIFLsoiaqDpvNIR5VeIMbFqdug79O2e3F1Op/f7LZxZUVtOpbROwpPem7FPamjwTCcpncgxM0o1PSZxSZKouqgYpukI1OWG3EqZBnK5jzZ6j6YnHyM1kU22c5P5IvBORdafZzciJkYXbERu4vtuzCdEFdSpTdBb2r7zb9J61ASVYvFwsCgMbEdBiGsMpsYXFZGV9eUCghvVR+zcwKbbDPJIwJcpuW3iWGEUuwMfQ5j05ahRC9oTXiEuBUajWIPJVG16NVG0J0ECamWVwCcHfkyNKPmtnjf6IvFTbaJV7T8RtaagL64g7cNz2dS7SdCbqIyB+yhJKoWWg9FiC1NpQmnRfEoXPRKy2pKZWRjiD6s0ae9hV4Yln2+2d0x4OBy5AoMLnwaxxXezY+DkA6Abv3CHkqiaqEr8wipj2GAxBI/XJ//HoxRvZu93+35jd+nboK0M/iW5k2dWzwCsS3wVcxJnQatmdfs4xPSUdBIFHsoiaqFFpUT0rjSYjNO93gYqin3Nqt9l0uNT9fFqyqa1Ud56Fjcpn8DH+ZGNas9IR0RJVHsoSSqFhqJIuTWDDozzhkGIWvZ27DIbj2txknOQB9j/SvnpHwPjMw8d8t9Ga4A+yPWYnDWaqSoJW2KmRB3R0kUeyiJqsWop8tECWmOjAIRLk55E7p+Y2/ZbkF+/av7xnnGQGjWN7qP0TsaT3i+iVVpo6j2EyHNoDHQVeVsoSSqFouFLs0jpLmU5UacClyKstufbrRNjyuKetumVFU22j47YjZGKTZgT3GQXWIkpCMwt7xSCLETSqJqYSiJIqRFLGYGlxWdkbzsQ5gDI+o9z01IQ1eTv/WxhCfG6Kz6V+UxQhm+CX0B49OWoNRAtZ8IaQkL1eZhDSVRtVASRUjr5BcAZ4e/BM3o222fYBgsKuxkfTjGKxYSg8amiSagP5bx3sRLmc2/8o8QUsNkoaEotlASVQv9HhLSepoqE04J41CweLNNTane16qs/47T6Kz/ZsDBpcg7MbjwSZyk2k+EtBpN57GHkqhaaCSKkDZigKRiX1yb9z6M0X0AAPyrKehs8oGQK8T4G1flmaVB2Br4Guam3ka1nwhpI1rPyx5KomqhX0RC7ENeYsLp7muhnHofYDZjcUkURnnHQqqvRFnoeEzRvo7tuZ3ZDpMQt2Cizy7WUBJVC41EEWI/Bp0Z5/UDkLXsP+idwcUkPYN9EY9gSNb9SNdQ7SdC7IUWlrOHz3YAzkQgKEBQRALbYRDiVqoAiHrfhqRAT5zzi8KgaLYjIsS9+HlL2Q6hw6IkqhatKh05V/eyHQYhbmX04MUQiAIxgr8bpV5zsEsZAvreTIj9hHrQpBJb6CdfC4dLPw5C7MXDwxvzRj+FiPIoZAvLUFYciBkVa7BJ9gsCBPReI8ReuFTYnzV0JquFy6OrhAixh26xIzC728MQFHBh4TJIL8tGUiIXPJ4UMZVf4zXmSYyS0a0qCLEHPoeyKLZQElULh0M/DkLahMNB/MhVGIgJYJTVN/QuDzVCrdHAZAL4/EEAAKkxHQ9VLsGD3kkQ0AcAIW3Co/cQayhrqIVL03mEtJqfXzgWjXgOfkX+QK0rXbM9yq3/Li2JtP6bAwZjKl7AG+JP0FlE7z1CWovePeyhn30tHJrOI6RVBva9DVNC7wKnyGyzneEwSCvPtj5OSuKDy7UtbxCi2YsNhvsww6sKhJCWo+k89lASVQuNRBHSMny+ELPGPIpuVf3BaMz1nleEmFCprkmO9HoGQsGAeu2EFjmWKe/Cs57/QsqjDwRCWoKm89hDWUMttLCckOYLD++J+QOfhke+uNE22TJFvW3yssYrlfdV/Qdbea+ij4ddQiSkQ6Cr89hDSVQtAlHjHwaEkBpjhizBWOlcQG68Zbs0ZU69bYkJAnA4wgZaV/PRn8fTmuVY7l0I+mwgpGlSHn2Us4V+8rVIPL3YDoEQpyaV+mLe6KcRXtYZjPHWt46vCDZBqVLW267TASLRgFvuy2N0mF6xFq/I9iCQakoRckt+AqqbzRY6O9Ui8aIkipDGdOsyErO6PARBQfPGh3K8Khp9TqFo3r1foit34FXL4xjjeesRL0I6Mn9KolhDSVQtEk9vtkMgxOlwOFxMGXkfBjLjwaiaXyAzrbL+VN5NSYkicDjNO/FLTVlYrVqKNd6JENICWkLqoSSKPZRE1UIjUYTY8veLwMIRz8K3yM+m9lNTVAEmlFfUX1R+U1UVA5GoX7P744DBqIoX8YZ4O6LEdNoipDY/ISVRbKGzUS1imYyqlhNyw8A+tyE+5M56tZ+aI8e3/lqoulTK2Bb3G6z5Bxv092KmV2WL9yXEXfkJ6MpytlDGUAuXy4NIKmU7DEJYJRCIMWv0Y+im7g9G2/IECgDS1XlNtklK8kBrTkECSzmWKlfiOa9jkFFNKUJoYTmLKImqQ+JF66JIxxUR3gvz+z8JjwJRq/uo8jOjtFzeZDul0gKxuG+rj9NH+Q628l5BX6opRTowGY8LERWKZg395OugMgekoxo7dClGS+eAKWvblXC5/qpmt62s7NqmY3nrL+IpzXLc4V1ANaVIh0SjUOyiJKoOSqJIRyOT+WL+6GcQJu8ENFH7qTnStfnNbpuS7AG0Mf3hMTpMq3gYm6W7qaYU6XAoiWIXnXHqoCSKdCTduozEzNiHwC+wT38abzOK5MXNbl9ezkAs7mmXY0dV7cTrlkcxlmpKkQ6EFpWzi5KoOqjMAekIOBwupo66v8W1n5qSG9jyq+bU6h52O77ElIMHVEux1juBakqRDoFGothFSVQdHjQSRdycv38kFg5/Fj6Fvi2q/dQcGfqWD2mlpnjaNQYOGIyseAlbRB8immpKETfnTzWiWEVnmDro6jzizgb1m4744BXgFLeudMGt6DwtyC8tbPF+paUWiEXd7B5PkPYA1uvvwSyv5i90J8TVULVydlESVQetiSLuSCiQYPaYx9C1sm+raz81JS+4qtX76nS97BhJDYFFgSXKu/G81xF40p3uiRui6Tx20VmlDkqiiLvpFNEH8/o9AUl+62s/NUeGseWjUDelpTn2fddb+R628DagP9WUIm6GFpazi5KoOmg6j7iTsUOXYaTHLDDljr1iTe/BILek+aUN6iosZCASRdsxovq89ZfxpGYp7vTOpxMfcRs0nccuOpfU4ekfAC6PMnvi2jxl/jdqP0XapfZTU/JDq8AwbVukbtC3vnp5c3EZA6ZWPILN0h8QRDWliBvoJBGyHUKHRilsHTw+H95BIVAUtv5btTtLLy3D4aQM5CuUUOn0WDl6MPqEh1ifZxgG+66n4nRGDjRGIzr5+WDeoD4I8b71FVhX8gqx91oKyqo08Jd5YFqf7ugbUdPvhex8/HElCQazGcOiIzGrf01toXK1Bp8cOYPH4kdDLBDY/0W7mJ7dxqC/eDyYAvuVLmhKprmozX1kZPog2rGDUVadq77Da/wT+NpzC45W0oeQ4fJ5qL//GqbUBFjK5PDe9DbEYyZan2cYBuqvPob2j92wVFZC0LMPvB55DvzoW99EWnd0P6q++AjmgjzwwiIgu2ctxGMnWZ/X7v8TVZ++B0anhWTaXHiuftz6nLmoAIqnH4Tf9h3gSmX2f9FuQMbjIlREv79soq9iDfALj2Q7BKdlMJkR5uOF2wf1bvD5Q0kZOJqSidsH9cajcWPgJRbhkyOnoTM2/oGeJVfg25MXMbhzOJ6YMhaDO4fjm5MXkF2mAACo9Qb879wVzOrfE/eNG4ZzWXlIKKgp6Lj7/DXM6Ne9wydQ1bWfHkA/82gwle2XQBnEDLJLmr7hcFPychmIhO333rtZU+ph76sQdfCaUoxOC0FsN3g+/GyDz2u++xKaH7+F58PPwn/7t+D6+UPx9GpYNOpG+zRcvwzlpmchiZ8B/0+/hyR+BpSbnoEx8SoAwKJUQLVtEzxXPw7fLR9Bu+836E8ds+6veudVyFY9QgnULXTxELMdQodHSVQD/MIj2A7BafUMDcK0vt3RNyK03nMMw+BYaiYm9+yCvhGhCPX2xJJh/WEwm3Exp/GRvWOpmegaHIDJPbsgyEuGyT27oGtwAI6lZAEAyqo0kAgEGNApDJ38fNAlyB/FquorwS5k54PP5TYYT0cSENDpRu0nH8Dxs3c2CsPUsFjsc1CjqZ9d+mmJERUb8IboA8R04JpSouFjILt3DcTjJtd7jmEYaHbvhHT5vRCPmwx+dBd4P/MKGJ0OugN/NdqnZvdOCIcMh3TZveB3ioZ02b0QDhoG9Y87AADmgnxwpTKIJ06FoEdvCAcMhSkrAwCgPfAXOHxBg/GQGl08HHuxCGlaxz1r3II/jUS1Srlai0qdHt1DAqzb+DweYgP9kSVXNLpfdpkC3YMDbLZ1Dw5A1o2RqABPKQwmM/IVSmj0BuSWVyDMxwsavQF/X09pdFSsoxjcbwbigu5wSO2n5shEid36ys7ys1tfLRGkPYiX9fdgjreSleM7M3NhPizlcgiHjLRu4wiFEPYfDOP1y43uZ0y4AuHgkTbbhENGWvfhRXQCo9fBmJoEi0oJU/J18GO7wqJSQv3Fdng+0vCoGKnRTUojUWyjNVEN8AujkajWqNTpAAAyse23I5lYCIVae4v99A3sI0KlTg8A8BAKsGRYf+w6cxlGsxmDO0ege0ggvj9zGWO6RKFMrcHn/56D2WLBlN7d0D+yY4xKCQUS3DZsNSQFQjBgJ4EyihhkFufYrb+sLCC2SxgMBjvdzK8FBBYFFlXcgz5ea/G+ZjJU5nYe0nNSlnI5AIDra5vgcn39YS5uvKyFpVwOrp+/7T5+/rAoyqr/7ekFr2c2QfXGS2D0eojjZ0I0dBSUWzfA4/YlMBfmo+LFx8CYTJDd9QDE4+Pt/MpcH41EsY+SqAbQmqi2qbe6hAE4Taw5aejp2pv6RoTYLDRPKylDobIStw/qgzf+PITlIwbCUyzCeweOIybQD55i9z65dIrog1HBc8EUsHuz3aIwLcyF9k3gLOb+ANo/ibqpl+oDbBEdxSfil3FR3bHXStmo+yZlmIbfuLfCMKj9zhaPnWSz0Nxw6RxMmanweuQZyFfMhveLr4PrG4DyNSsg7De4XiLX0XWlNVGso+m8Bog8PCCjN2uLeYqr39A3R5BuqtIbILvFFSSeYhEqtXX2aWB06iaT2Yw9F65hwZC+kFepYWYYxAb5I8hLhgCZFDnlFW17IU5u/LDl7VL7qTkyufabyrspNzfQ7n22lJf+Ctapl+Eu79wOf5Lk+lVPtVvKy2y2WyrKb5nUcP0C6u+jaHwfxmCA6t3X4PX4izDl54IxmyHsPwT8TlHgRXSyLkgn1QQcDqIl7v1l0RV09PNDo2hxecv5SSXwFIuQUiy3bjOZLUgvLUNUgG+j+3X297XZBwCSi+WI8m94n38S0tAjJBARvt5gGAaWWvWJLAzT5npFzspT5o8Fo59BSGlEu9R+aopZwCCzxH5TeTelpVkgELCfSHEZA6ZUPIZXpd8hWNhxT5W80HBw/QJgOH/Kuo0xGmG4fB6C3v0b3U/Qq5/NPgBgOH+y0X3U33wK0bDREHTrCVgsgLnWCKfJBMZOFy+4iyiJEHwujZSyreOeGZpAU3oN0xtNyFcoka+oXoBbXqVBvkIJhVoLDoeDsV2jcSAxDVfzilCorMT3Zy9DyONhYKdwax+7Tl/Cn1eSrI/Hdo1CSrEcBxPTUaKqwsHEdKQWyzG2W1S94xcpK3E5twBT+1TfsDbIUwYOgNMZOUgoKEaJqgqRvj6O/BGwome3sZgRsxo89ma56ikK08FodMRoGAfAQAf02zqdqn7Aa+aHMcHTwHYoDmPRamBMS4YxLRlA9WJyY1oyzMWF4HA48Ji/DOod/wfdsYMwZaZBteVlcMRiiCdPs/ahfP1FVH76nvWxx7ylMJw7BfWuL2DKyYR61xcwnD8D6YLl9Y5vykyH7vDfkK18CADA7xQFcLjQ/vkT9KeOwZSTBUH3jn0BSV00leccaE1UI2hxecNyFUr893DNt8tfLycCAIZERWDJsP6Y2CMGxhvTbVqDEZ38fXDf+OEQ17o1gUKjtVkjFRXgh+UjBmLvtWT8fT0Z/lIPrBg5EJ3rjEQxDIMfz13F7AG9IOJX9yfg87BkWH/suXAdZosFtw/qDW83OrlwuTxMGXEfvIu927X2U3NkCUod1nd+XjCCgh3WfYuJTXm4T7UU/XzW42NVf+jdbLTTlJwAxbr7rI+rtr8FABBPnQXvZzbBY8lKMHo9Kt99HZZKFQQ9+8B363ZwPaTWfcwlRQC35nu5sM8AeL/0Oqo+/whVX3wEXlgkvF96A4KetpXpGYaB6u1X4PnQk+BIJAAAjkgM72c2QvXu64DRCM9HngEvMMiRPwKXQ4vKnQOHcde5jzbKvnoJP25+ke0wSAcWGBiFSTHLgRLnSp4AwMxnsFN6HHq9vunGrcDhMJgw8Q+YTI2XxmCL3GM83mceRZqOTp2EPe/37ISFIbR2l200ndcIWhNF2DS0/yxMDljmlAkUAJSE6R2WQAEAw3DA5QxyWP9tEaA5ghd1d2OuVwXboZAOjKqVOwdKohrh6RcAocSD7TBIByMUemDOmHWIUfUCo2On9lNzZAvlTTdqo8JC5633JWCUWKi8Fy96HYA3j06jpP11pek8p0Dv/lvwCwtvuhEhdtK5U3/M67sO4nznvgeghcsgXW7/q/LqSk7mgMfzcvhx2qKn8iO8wXsJA6U0tUfaT6hIABmfx3YYBJRE3RJdoUfay/hhKzBCNM0paj81RR5ugFbXeAV6ezGbAT7POaf0avPSX8MT6iVY6Z1DJ1TSLmgUynnQe/4W/CM6sR2CU1HrDVj/yz8oV2vYDqWer06cx5HkDLbDaDFPzwAsGP0sQkrDAJNrjGZki8uabmQnRcWuMRrMYUyIr3gcr0p3IcTFakpZlBUomTcJ5iInqp9xQ8WGJ6H+4Ru2w3A6dM8850ElDm4hrFsPtkNwKgcT09A7LAh+0uq1Yj9fvI5MeTmKlFUI9pJh3ZSx9fYprFDhp4vXkVNeAQ+hACNiOiO+VxebEgfpJWX49XICipVV8JKIMKF7LEZ16WzTz5W8Quy9loKyKg38ZR6Y1qe7zW1g4nt1xX8Pn8LwmEiIBc49HXZTr+7j0E80FkyBcy4ebwjDYZBWlt1ux0tK5GLMWCnMZnW7HbMtOlX9iFf5p/Ct51YcqnSN0QL1zs8hGjkOvJAwAIDqg60wXr0EU1Ya+J2i4f/p9/X2MWakovK9N2BMug6upxcks+ZDuuJ+m/e14fI5VH70NkxZ6eAFBMJj8V3wmL3Qph/d0f2o+uIjmAvywAuLgOyetTa3gZGuuB+KJ+6HZPrt4EplDvoJuJ4hXtKmG5F24VpfmdpZSJfu4LnIB7KjGU1mnMnMxbDoWqNzDDAsKhIDGrnhr85oxCdHz8BLLMajcWNw+8DeOJKcgSMpmdY2ZVUafHbsLGIC/PD4lDGY3LMLfrl0HVfyam5smiVX4NuTFzG4cziemDIWgzuH45uTF5BdVnP5e5iPF3ylElzIdr5v03VxuTzcNupB9DWOdLraT02Rhxqh1rTfSKTJBPD5zj+lV5vYlIdVqmV41PsSxE5eUZrR66D962dIpt9eayMDybQ5EE+Y0uA+FnUVKp56EFz/QPhv/xaeDz8Dzf++gabWiJG5MB+K5x6GsO9A+H+yCx7L7kHlB1uhO7rf2sZw/TKUm56FJH4G/D/9HpL4GVBuesbm9i6C2G7gBYdBt/9P+794FzbShxJKZ0FJ1C3wBQKExHZjOwynkFRUAi6Xa3P7lrmDemN01yjryFRdF7ILYDSbsWRYP4R6e6JvRCgm94zF0ZQM661ZTqZnw9dDjDkDeyPYyxPDYzphaFSkzdTcsdRMdA0OwOSeXRDkJcPknl3QNTgAx1KybI7XOywYF3OcO4kKDIzCwqHPwrvQC3CN2TsbOdLydj+mvNQ11yYOq3gFW4TvoIvEeRMp/enjAI8HYa1bsXg9/Aw85i4GL7ThMi+6/X+CMejh/cwm8KO7QDxuMqTL7oHmh2+t72vNbz+CFxQKz7VPgd85Bh4z5kEybQ40//va2o9m904IhwyHdNm94HeKhnTZvRAOGgb1jztsjicaNR66g3sd8OpdU5REiGARfbl3FpRENSGiZx+2Q3AKGaXliPD1btE+2WUKxAb6g8+ruYqke0ggVFo9ytXaG20q0C3E9j5p3UMCkVuuhPnGvbKyyxToHhxg2yY4AFlltoUYI/18kFteAZPZOUsDDOs/26lrPzWF4TBIUzj+qry6EhN54HJdcw1IgPYoXtLehdu9na9oKAAYr1yAoHuvlu2TcAXC/oPBEdbcVFw4dBQsZaWw3FhXZbx+GcIhI2z2Ew4ZBWNyIhiTsaafwSPrtBkJ4/XLNtsEPXrDmHQdjMF9b7vTEiO8aRTKmVAS1YSIHi07wbircrUW3i28Y3ilTg+ZWGizTSYWWZ+7+benyLZfT7EQFoaBWm+o1Y+oXj83+7jJWyKGyWKBSue4IpCtIRJLMWfMOkSrejp17aemKEJMqKyqbPfj6vWAQOA899JrKT5TiQUVq/CS5z/w5jvXKddcXACuf8tu9mwpLwPX199mG9e3unK2uby6fphFUQZeQ23MJliUFTf6kYPrV6eNnz8sCtsLF7gBQYDRAEu542uTuYLhPrQeypk41zvaCYV17wkOl35MRrMZfG7L65JwUGcq48YUFodj06ihJrb9NDAjUneT4MaIl9GJRqKiOvXH7b0ed/raT82RLWNvNKW8LIq1Y9tLD9V/sYXzIgY7UU0pRq8HR9iKBfB135BMA9sbm8Vs6M1s7YeptyPnxpcsRq9rUYjuitZDORfKDpoglHggKCqG7TBYJxUJoTW2rIaRZwOjRVU3bhUiu3FibLCNzgAuhwOpSFjTRlu3Tf3RKc2N4X6ZyDmuipow7E4MF00Do3D+2k/NkaZs/6m8mxKTBOBwXD8R9TRcx+PqJbjHOxvOUCqR6+0DplLVsn38/OuNClkqqtfK3Rx94vr6w1xuO6JkUZQDPD64Xt43+gmApYE2N0e1broZH9fH9obkHVGIUICoFs4IEMeiJKoZInr2ZjsE1oX7eKFYVdWifTr7+yKjtAwms8W6LblIDi+JCH5SyY02Pkgptj0hpxSXItLPG7wbI4Cd/X3rtUkuliPK3/akWqSshLdEbE2+2OLpFYgFo55FcGmoy9R+akpFsAlKlZK142s1DEQi153Sq43DmDC5Yh1ek+5AGMs1pfhdesCU3bL6aoJe/WC4cgFMrS9VhnMnwfUPBPdGmQRB7/4wnD9ls5/h3EkIuvcEhy+o6adum/MnIai1yB0ATJlp4AYGg+tNSRRN5TkfSqKaIbwHJVHdQwJRpKyExlBz4pRXqpGvUKJSp4fRbEa+Qol8hdKaNA3sFAY+j4fvz15GobISV/OKcDApDeO6xVjryYyM7QyFWotfLyWgWFWJMxm5OJOZi/Hda0b/xnaNQkqxHAcT01GiqsLBxHSkFssxtluUTYyZcgW6h9guQG9vvbuPx4zO94NX6B7J0005TrAwWqGIZjsEu4qo2oNXTGsx2Yu9aSrh0JEwZWXAUms0ypSfA2NaMiwKORi9Hsa0ZBjTkq1Jk3jyNHAEQqi2vAxTZhp0xw5CvfNzeCy8w/q+9pi1AObiQlR+tA2m7Axo//oZ2r9+hseiO63H8Zi3FIZzp6De9QVMOZlQ7/oChvNnIF2w3CZGw9WL9Rapd1TDvSmJcjac/2/vvsObKts/gH9Pdpu06d6DDkoHlNKWlrJ3mbIEEUEZIoqAyE9xiyi+ioq+LhQFhVdQVHxBBMGBIFaGgjKEFugupbtN23SkGef3R18CoS1dSU7G/bmuXtqTc55zh6bNnWfcD3t9TSppU31NNT54YO7/xuvt17uHfkdijwCkhDUXwtx4+Diyy1oueX964gh92YMiRQ3++9cFFFQq4CASIiUsCGOie7YstnnmIoprmottjmil2ObZgiIc/OcSKuvq4S51xPg+vdAn4EZ9KrVWi7V7f8bioUkIdjf/J1Y+X4CxAx6Ac5GTVZYuaM9un79RoTB/eYObyWQM4hM+B8ta5+rG2znl8iw+qI1Ho878L57KZfdCknoHHCff2fz9o/dDffZ0i/M8Pt+vL8ipzr6C2rdfuanY5p2Q3ttKsc33N0CTlwWeuyeks+e3LLb5609QfrIR2qKr4PsFQrbwYUiGjtI/zjapUDZjNFzWvw9RdKwpnr5VOdy/F6JkDlyHQW5CSVQHfbrqIVQWFnAdBqfSi0rx3dl0PJY6FLzbTQ7lwO9XcnHhWgkeGJZs9nt7e4VheMhsqy1d0J4aDy2+Uh7hOgwAwJixF9DY+BfXYZhEucMQvMc8iisN5v2TrDrxG2o3vQX3LbssbhFN/Z4vofr9CFxf/4DrUDjnIuAjfXBvg0SVcM+yfmMsGM2LAqJ8vZASGoSaBstbJcPn8TC1n/l/RklxUzHC7S6bTaAAIN9VwXUIejXVYVyHYDIeDb/h2Yb7MENu3h4/8YAhcJx0J3TlpWa9b4cIBHBa/gTXUViEJLmUEigLRD1RHZT+22F8/94GrsMgFkIskWJ84oMQF9r+9pN7/c6i1EJq9MjlDGL7bgega/dca3bJ+QG83TAe1Rrbfp6k454L88PDQV5ch0FuQT1RHeRPlcvJ/4QEx2Fa9KN2kUAp3bQWk0ABQHU1C4nE9n8Xe9V8hPXM00iUUhJFmqXQpHKLRElUBzl7eMLZkz4F2LsRyfchSTTOZmo/tafAvXM1hMxBqbSP/SydmtKxsu5uLJLnWERNKcIdRz4PfZxa36OUcIuSqE4IoFIHdkvu7IU7Bz4Fr1Ifm6n91BHZDYVch9DC5UtStF0O27YwrAYjFY/hX46fwZ/jmlKEOwnOjhDy7OM1b23ot7ITgvvGcx0C4UDvqOEYH7wY/CL7Glqpl+tQVF7CdRgtVFToIJFEcR2GWQXU7cGLmqUYzWFNKcKdYa5OXIdA2kBJVCeE9usPHp861u0Fny/AhEEPI0aVDFZpu6vv2nLV0/ybDXdUfV0k1yGYnURbhAXV9+BR59OQUK+EXRnnKec6BNIGSqI6QSKTUfVyO+HjHYY7E56A0zWZTRbP7Iispmtch9Cmy5ftdxPWxOp/4XXRBvRyoETKHoQ7ihHuKOE6DNIGSqI6Kbw/bT9g65L7TcNw11lAmf31Pl3X4KRDYanlJlFlZSwkYvuYYN4at4bf8XTDvbhTXtH+ycSqjfOgXihLRklUJ4XTHk42SyKRYerg/0MPRQRYlX3Nf7pVoXfnNpvmQmOjfc2LupWAVWKa4gGscToAFwH9KbdVlERZNvrN6yRnTy94BtvWRqgECO2RgKlRK+2i9lNHZKuLuA6hXZmZ9OYCABE1m7GeeQr9qaaUzfESCRDvTKUNLBklUV1AQ3q2ZeSA+egvGANWYR+1n9qjcmRRUGp5pQ1uVVTEQiwO5ToMiyBrysAK5WzcL8+mmlI2ZKy73OL2KSWGKInqgjAa0rMJcrk3Zg58Cp4l3oDWTmePt+Kabx2sZTeoJhUt9LiOBy1GKB7Hq45bqaaUjUj1cOY6BNIOq/lNmz9/PqZOndrh83Nzc8EwDM6cOWP0WLxDwiD39jF6u8R8+kSNxPjA+8Gzs9pPHZGtK+Y6hA7LyXXjOgSL41f3HV7SPIixzg1ch0K6wYnPw1A3y64P9cILLyAuLq7Nx7du3QoXF5du3aOz7/3m1ukkqrS0FEuWLEFQUBDEYjF8fHyQmpqK48ePmyI+i9VrwGCuQyBd0Fz7aRmiVf3B1tnv6ru2NElY5JUUcB1GhxXk6yASBXIdhsURa0twX/Vc/J/zn3CgmlJWaZynHGKeafs5jh07Bj6fj3Hjxpn0PtZg+PDhWLlyZaev6/RPaMaMGTh79iy2bduGy5cvY+/evRg+fDgqKys7fXNrFpEyhOsQSCf5+IT/r/aT1G5rP7WnyK8eOp119c5pNH24DsFixVe/ivXCNxBJNaWszh2eLia/xyeffILly5cjLS0N+fn5Jr+fLepUEqVQKJCWlob169djxIgRCA4ORlJSEp566ilMnDgRAPDmm2+iT58+kEqlCAwMxNKlS6FU3lgufb1774cffkBUVBRkMhnGjRuHoqIbq4G0Wi1WrVoFFxcXuLu7Y/Xq1S3maBw8eBCDBw/WnzNp0iRkZWV159+iU7xDwuDq62e2+5HuGdBvOoa7zLTr2k8dkQPL2+alPfl5HlyHYNHcG4/h6Ya5mCUv5zoU0kEuAj6Gu5l2PlRdXR2++uorPPTQQ5g0aRK2bt1q8PiRI0fAMAwOHTqExMREODo6YuDAgbh06VKbbebk5CA8PBwPPfRQmx/GvvvuOyQkJEAikSA0NBRr166FRtP+3+W1a9fCy8sLzs7OWLJkCZqamvSPqVQqrFixAl5eXpBIJBg8eDD+/PNPg+t//fVXJCUlQSwWw9fXF08++aT+vvPnz8evv/6Kt99+GwzDgGEY5ObmthsT0MkkSiaTQSaTYc+ePVCpVK03yOPhnXfewT///INt27bhl19+werVqw3Oqa+vxxtvvIHPPvsMR48eRX5+Ph577DH94xs2bMAnn3yCLVu2IC0tDZWVldi9e7dBG3V1dVi1ahX+/PNPHDp0CDweD9OmTTPrp+he1Btl8SQOzpg2+DEEK3rafe2n9qjFLHJLrWco77qcHBYikS/XYVg0PluPKYolWOu0H65UU8rijfeUm3zD4S+//BK9evVCr169MHfuXHz66aetLih55plnsGHDBpw6dQoCgQALFy5stb1//vkHgwYNwsyZM/HBBx+A18pQ5A8//IC5c+dixYoVuHjxIjZt2oStW7fi5Zdfvm2shw4dQnp6Og4fPowvvvgCu3fvxtq1a/WPr169Gt988w22bduGv/76C+Hh4UhNTdWPkBUWFmLChAno378/zp49iw8++ABbtmzBunXrAABvv/02UlJSsHjxYhQVFaGoqAiBgR2bJtCp3yaBQICtW7di27ZtcHFxwaBBg/D000/j3Llz+nNWrlyJESNGICQkBCNHjsRLL72Er776yqAdtVqNDz/8EImJiYiPj8eyZctw6NAh/eP//ve/8dRTT2HGjBmIiorChx9+CLncsCbMjBkzMH36dPTs2RNxcXHYsmULzp8/j4sXL3bmKXULJVGWLTQkAVMjV0BUSIu+O6LYr6FDnwgtkU7bl+sQrEJ4zSdYjyeQLNNyHQq5jSleLia/x5YtWzB37lwAwLhx46BUKg3eh697+eWXMWzYMERHR+PJJ5/EsWPH0NhouBH28ePHMWzYMKxatQqvvPJKm/d8+eWX8eSTT+K+++5DaGgoxowZg5deegmbNm26bawikQiffPIJYmJiMHHiRLz44ot45513oNPpUFdXhw8++ACvv/46xo8fj+joaHz88cdwcHDAli1bAAAbN25EYGAg3nvvPURGRmLq1KlYu3YtNmzYAJ1OB7lcDpFIBEdHR/j4+MDHxwf8Du6T26U5UdeuXcPevXuRmpqKI0eOID4+Xt8VePjwYYwZMwb+/v5wcnLCvffei4qKCtTV1enbcHR0RFhYmP57X19flJaWAgCqq6tRVFSElJQU/eMCgQCJiYkGcWRlZWHOnDkIDQ2Fs7MzQkKaC2Cac1zXI6gH3AOCzHY/0kEMg1EDFqA/n2o/dUYur5TrELqsoMCT6xCshlR9Gctq78YD8kwIaKqUxXET8jHYxbSr8i5duoQ//vgDs2fPBtD8HnvXXXfhk08+aXFubGys/v99fZt7fK+/XwPN77mjR4/Gs88+azCi1JrTp0/jxRdf1I9qyWQyfe9PfX19m9f17dsXjo43io6mpKRAqVSioKAAWVlZUKvVGDRokP5xoVCIpKQkpKenAwDS09ORkpIC5qaaW4MGDYJSqcTVq1dvG3N7utSvK5FIMGbMGDz//PM4duwY5s+fjzVr1iAvLw8TJkxA79698c033+D06dN4//33ATT3Pl0nFAoN2mMYptN1aSZPnoyKigp8/PHHOHnyJE6ePAkABuOk5kC9UZbFxcUHM1OehEeJF9V+6gStkEV2qfVOLM3MZCEUUiLVUTxoMUzxBF5x+BQBYhresySTPF0gMPFQ3pYtW6DRaODv7w+BQACBQIAPPvgA//3vf1FVVWVw7s3v19eTkJunzXh6eiIpKQk7d+5ETU3Nbe+r0+mwdu1anDlzRv91/vx5XLlyBRJJ5zdZvjl3YG4pSsqyrP7Yzf9/8+OtXddZRvntiY6ORl1dHU6dOgWNRoMNGzZgwIABiIiIwLVrndvEVC6Xw9fXFydOnNAf02g0OH36tP77iooKpKen49lnn8WoUaMQFRXV4gdvLlFDRgBUUdYixEaPxriARVT7qQuK/RoNPuhYHwZAHNdBWB2/un14Sb0EY53b7gUg5jXTx7S1zzQaDf7zn/9gw4YNBsnM2bNnERwcjB07dnSqPQcHB+zbtw8SiQSpqamora1t89z4+HhcunQJ4eHhLb5am0N13dmzZ9HQcKPu2YkTJyCTyRAQEIDw8HCIRCKkpaXpH1er1Th16hSiopr314yOjsaxY8cMOmuOHTsGJycn+Pv7A2geMtRqOz/M3akkqqKiAiNHjsT27dtx7tw55OTk4Ouvv8Zrr72GKVOmICwsDBqNBu+++y6ys7Px2Wef4cMPP+x0UI888gheffVV7N69GxkZGVi6dCkUCoX+cVdXV7i7u+Ojjz5CZmYmfvnlF6xatarT9zEGF28f9Ogbz8m9STM+X4SJg5YhqjGBaj91UZ6wjOsQuq3wKhXA7QqRthT3Vc/DY85/UE0pjsXIJOgvl5r0Hvv27UNVVRUWLVqE3r17G3zdeeed+nlEnSGVSrF//34IBAKMHz/eYEX+zZ5//nn85z//wQsvvIALFy4gPT0dX375JZ599tnbtt/U1IRFixbh4sWLOHDgANasWYNly5aBx+NBKpXioYcewuOPP46DBw/i4sWLWLx4Merr67Fo0SIAwNKlS1FQUIDly5cjIyMD3377LdasWYNVq1bpk7cePXrg5MmTyM3NRXl5eYcXqXV6dV5ycjLeeustDB06FL1798Zzzz2HxYsX47333kNcXBzefPNNrF+/Hr1798aOHTtuO8msLf/3f/+He++9F/Pnz0dKSgqcnJwwbdq0G0HzeNi5cydOnz6N3r1749FHH8Xrr7/e6fsYS9zYiZzd2975+UbgzoTHIaPaT12m5bPIKrPeobzrLl9mIRC4ch2G1epXvR6vCV9DFNWU4sx8f9OX69iyZQtGjx7dYrEW0Dzn+cyZM/jrr7863a5MJsOBAwfAsiwmTJhgMA/6utTUVOzbtw8//fQT+vfvjwEDBuDNN99EcHDwbdseNWoUevbsiaFDh2LWrFmYPHkyXnjhBf3jr776KmbMmIF58+YhPj4emZmZ+OGHH+Dq2vz3wN/fH99//z3++OMP9O3bFw8++CAWLVpkkLw99thj4PP5iI6OhqenZ4fnVzOstWySZcFYnQ6bVyxGTZn11dixZinxdyK4rifYJhq+647iIBX2laa1f6IVGDWqEE3qX7gOw6ppGUd8L9+AndVeXIdiV5wFPPw9MAbSDq4KI5aBZhQaAcPjIXY0lc03F4mDM6YNehxBVWGUQBlBrtj6h/KuKy6mArjdxWfrMVnxENbKvoMb1ZQym5nebpRAWSH6DTGSPiPHgn/LqkNifOEh/ZtrP12jl64x6Hi2MZR3XUYGAz7ftJWe7UV47Va8isepppSZmGMojxgfvRMZiaOzHBHJg9o/kXQNw2D0gIVI4I+i2k9GVO7XhIbGhvZPtBJaLSDg00IPY5GqM7Gs9m4skV+hmlImNMhFhp7Szi/xJ9yjJMqI+tIEc5NwcfHFzAFPwb3Ek2o/GVmeQwXXIRhdSak/1yHYFB60GKp4Eq86bEEg1ZQyCeqFsl70G2FE/r2i4NkjlOswbErfmLEY578AvGIaUjA2lmGRWWE7Q3nXZaTzwOc5tn8i6RTfuu+xtmkxxjm3XHVFus5HJMR4j5Yr5Yh1oCTKyOLGTOA6BJsgEIgwadByRDb0A1tPCZQpVPiqUVdve2+IajUgENKQnimIdeWYV30vHnc+AUeqKWUU9/i5mbxCOTEdSqKMLGrwcIgdTVsszdb5+/XCnf1WQ3rNkWo/mVCetJLrEEymvIz2tDSluOrX8ZrwFURTh1+3CBhgnh8N5VkzSqKMTCiRIHroSK7DsFoDE2ZiiNN0sOU0edyUWIZFZpXtDeVdl57OB49HE3VNybXxTzxZPxez5SWgfpSuSfWQw0dMq7qtGSVRJtB3LA3pdZajozOmD3ocgZWhVPvJDBTeGtQq297jytqpVIBI2I/rMGwen23AZMVSrJXthbuQ3k46awFNKLd69Ko3AXf/QATGxHIdhtXoGZqEOyJWQEi1n8wmz4mbDbvNqaLy9ltJEOMJq92GV9jHkEI1pTqsp6MYg12duA6DdBO9a5lIHPVGtY9hMDplEeJ5I8FW0/CdOWVW2+5Q3nUZ6SIwDA2VmItUnYWHa+/Cg/LLEDI0wNee+6gXyiZQEmUi4f1TIHNz5zoMi+Xm6tdc+6nYg2o/mZnCSwNFTTXXYZhcfT0LsTiO6zDsCgMWQxRP4VWHjxFENaXa5MjnYZaPG9dhECOgV7mJ8Ph89L9jBtdhWKS4mLEY6zff7LWfThScwYJdTyLh/WkIXD8UBy//ZvA4y7J4M+0TJLw/DeEbRmPm5ytwqSyn3Xa/v3QEIzfPQ9gbozBy8zwcuHzU4PHdF35E0sYZ6P32RKw7vNHgsYLqIgz9aA5qVeYrNVAgV5jtXlyrqgox273OnWvAs88U465ZeRg9Khu/pxn+TFmWxbZtlbhrVh4mjM/BqlXXkJvb1G67R48qsXBBAcaPy8bCBQVIu6XdQz/X4u7ZeZg2NRebNhkWTy0uVuO+ewtQV2feeYY+dQewtmkxxlNNqVYt9PeAs4D2ybMFlESZUOzo8dQbdROBQIRJg1egF0e1nxqaGhHlFYZ1o1e2+vgHJz/Hx39+hXWjV2LfvR/BU+qGOV+tglJV32abpwv/wdJv12J6TCp+WPAJpsekYum3a/D3tYsAgMp6BR4/+BqeHbEU22e9gV3/HMShrOP665/+4U08NWwJnMTmK4uRWVtgtntxLSNdAoYRmOVejQ0sQsNEWLa89WGaL3dW45td1Vi23APvb/SHmysfT6wuQn192wnOxQuNWPdSKUaPkWHTRwEYPUaGl14sQXp6IwCgulqLDRvK8cASd7zyqg9++rEWJ07ceL2+/e9y3L/YDVKp+f/Ui3TlmFt9L55w/h1SPg3vXSfj87A0yIvrMIiRUBJlQgKhEMnT7uI6DIvg7xeJGf1WQ1rowFntpxFhA7B66GKM7zWsxWMsy2LLqa+xPGUexvcahkjPULw18Wk0qlXYk/5Tm21uPvU1hvRIxLKUuQh3D8aylLkYFJyAzae+BgDkKa7BWSzDHVGjEOcbhZSgfrhSngsA2H3xJwj5glbjMZUady0qFLZbH+pWSiULsbiPWe6VlOyIhQvdMGRIy4SYZVn897/VmDPHFUOGSBESIsLqJ7zQ2Mjil0PKNtv85r/VSEhwwJw5rggKEmHOHFf0i3fAf79pHo4tKlJDKuVhxAgZIiMl6BvngLy85t6tQ4eUEAiZVuMxp9jqN7Ge/y/0pppSAIDFAZ5wE5onsSemR0mUifUZOQbOnvb9qWNQwl0YIpsGWHDtp/zqIpTWVWJoSH/9MbFAhOTAvjhd+E+b1/1VeMHgGgAYFpKkvybELRAN6kb8U3IZVQ01OFeUgUjPMFQ11GDDb59g3ZiVJnk+bSlwU5j1fpagpiac6xBQVKRBZaUWCYkO+mMiEYPYvhJcuNDY5nUXLzYaXAMAiYkOuHBBBQDw9xdCpdLhyhUVamq0uHRJhdBQEWpqtNi2tRLL2+gVMzdX1Smsrp+LOfJiu64p5Szg4cFAT67DIEZESZSJ8QX22xvl6CjH9MGrEVDZA6zasms/lSmb55J4OBpO9vSUuqFU2XbPTVldJTykrgbHPKSuKKtrvsZF4oQ3Jz6NlftexuT/LMGM3qkYHpqEdYc3YkHCdORXF2Hcp4swast92J9xxLhPqhVZdYUmv4elyUh3ANd/6qqqmoevXV0N58G4uvJRWdX20HZVpRauroa9Fq6uAlRVaQAATk58rH7CC+vXl2LZw4UYM0aG/v0dsWlTBaZOlaO4WI0lS67i/kUFOPpr2z1e5sBnGzBR8TBeku2Gp53WlFoS4AU59ULZFPppmkHv4aPxx7dfo7qkmOtQzCYibADinUaBLdRwHUqn3Loym2VZMO0s12Zu/WzNsgbHxkcMxfiIofrvj+f/jYyybKwbsxKDP7ob701eAy+pGyb/ZwmSA/u2SMqMpc5Vi9LKMpO0bcmqq1lIJDFobDzPdSitvL5aHmvvGrAscNPra/BgKQYPvjFkd+ZMA3Jy1Fi+3AP33VuAp5/xgpsbHw8/XIg+sQ4tEjlzC6ndjn8Jf8ensldxTGk/b0GuAj4eoF4om2OfHwfMjMfnY8D02VyHYRYMw8OYlPvRD8PBVltPAuUpa14AcL0H6bry+ip43iap8ZS6tXKNos1ESKVpwjM/volXU/8PuVWF0Oq0SAmKQ5h7EELcAvF30cVuPpO2FXjUmKxtS6dURnB6/+uJS2WlYa+TQqGFq0vbSY2rGx+VlYa/R1UKbZuJUFMTi3feLsejKz1wrVANrZZF374OCAwUISBAhIz0tocOzclRnYOltbPxkDzDbmpKPRjoBSdakWdzKIkyk+ihI+Dq6891GCbl5uaPmQOehFuxO6CzrtpPQXJfeEnd8FvuKf2xJq0aJwvOIsG/d5vXxfvH4LfcPw2OHc35s81r3j62DcNDk9HHpxe0rBYa3Y03VY1OA63OdMOeWQ3XTNa2pbt8SQZwOBvH11cANzc+/jrdoD+mVrM4d7YRMTFt7/EXHS0xuAYATp9qQEyMuNXzt2+vQlKSI3pGiKHVAdqbcjathoUJX16dxoDFYMUzeFWyCcE2XlPKTcjH/QGWMT+NGJdtv3ItCI/HR8oM2+2N6tdnHMb63AfGzLWfOqOuqR4XSq7gQskVAM01mi6UXEFhTQkYhsGixJl47/h2HLh8FBll2Vi1/xVIhGJMjRqjb2Plvpfx6q+b9N8vSrgTR3NOYeOJHcisyMPGEzuQlncK9yfObHH/S2U5+C7jFzw2eBEAIMwtGDyGh51n9+FQ1nFkVeSjr2+kSZ57vVyHonL7GU6+VUWFDhJJlEnv0dCgQ2amCpmZzZO+i4rVyMxUoaREA4ZhMH26HJ9/rkBaWh1ycprw2mulkEgYjBwl07fx6qul2Lz5Rs/m9OlynDrVgJ1fKJCf34SdXyjw118NmD5D3uL+ublN+PWIEvfNb+4FDQoSgmGAA9/X4MSJeuTnq9GrV+vJF5d86n/A2qZFmOhsu3s5PhzkDSn1QtkkhmVZ6+oysGKsTodtjy9DxVXb2XJDKJRgXNISOF5r+9O0pTie/zdmffFIi+N39h6HtyY+DZZl8dbvn2LHmb2oblQizi8K68Y8ikjPUP25Mz9fgQC5D96a+LT+2P6MI3j9t83IV1xDsItfq2UUWJbF9B0P4+EBczE6fKD++M+Zx/DsT2+hSavG40Pux919J5ngmQOXw6tx9Oqp9k+0YYMGKcHj7zZZ+2fONOCx/ytqcXzsWBlWP+EFlmXxn/9UYf++WtTW6hAVJcbyFR4ICRHpz1216hp8vAVY/cSNFb1Hf1Xi00+rUFSkhp+fEAtaKaPAsixWPnINd9/tggEpNx47cbwO77xTAbWaxYIFrpgw0dkEz9x4zjs/ivfqh0BpQ7sYeIoEODkgGo586rOwRZREmdml479h37/Xcx2GUfj7R2GI7wywFZZbuoA0OxiUjqul9jucBwBeXjz0itzGdRikHQpxAj4QPI1/2q5xa1VeDPfDA4H2XebGllFqbGYRAwbDM6gH12F02+DE2RginUoJlBVolOnsPoECgNJSHSTinlyHQdrhojqN1fX3YK68yOprSvmKhbjXj+ZC2TJKosyMYRikzJzDdRhdJpW6Yvqg1fCvCLb42k+k2VUfbusDWZLGxmiuQyAdwGcbMV6xDC/J/mvVNaWWB3lBQsN4No1+uhzomTQQXiFhXIfRaRHhKZgcvhTCa9b++dC+5Gjsd0L5rbKyXLgOgXRCSO0O/Ev3KAY7WV+Pt79YiLl+tHeqraMkiiMDZ97DdQgdxjA8jE1ZjH7sMLA11lP7iQBNjjrkl1zlOgyLce2aDmJxCNdhkE5w1OTiwZq78bA8HSIrqim1soc3RDx6i7V19BPmSFhCEoJj+3EdRrvc3QIwM/lJuBa7WV3tJwIU+taD1o4Yampqu+4XsUwMWAxUPIv1ko3oIbH8t63eMgfM8aVeKHtg+a9GGzZq0UMQCEXtn8iRfn3GY4zPvWBKLLf2U0dVNVQj7t07UFDdcgk615bsfg4f/fGlSdrO0dFQ3q1ycoy/rU51tRZ3zshFcbHlDTutfaEEu75WcB2GUXjV/4wXVIsw2dlyq+8zANZHBIBvRb1mpOsoieKQq48f+k+5k+swWhAKJZg8aCUilLFgG6w/gQKA945vx+iwgQiU+wIA1vz8NiZsvR9hb4xC6qcLW70mvSwLd36+HOEbRiPx/en49+9bW/TqHM8/gwlb70f4G6Mx6MO78Nnf37Zo5/tLRzBy8zyEvTEKIzfPw4HLRw0eXzloPt49/hlqVXVGerbNmiQscksKjNqmLSjIZyESBRq1zZ1fKDBggBQ+PkIAwPvvleOhB69i/LhsLHmg9eHU7OwmrHr0GiaMz8Fds/Lw2X+qWry+zp5t+F87OZg7Nx/ffdcyeTh6VImFCwowflw2Fi4oQFqa4eto3jwX7NihQF2dbSwEEeoqMbt6AZ5yPgoZ3/ISlbl+7kiQS9s/kdgESqI4ljR1Jlx9/bgOQy/QPwYz+j4Gx2uWV9m4qxrUKnx5br9BIUsWwKzYCZgcObLVa2pVdbjny/+Dt8wD++79CC+NfgSb/tiJj/680WOUr7iG+3atRlJALA7M34xlKfOw5ue38f2lI/pzThf+g6XfrsX0mFT8sOATTI9JxdJv1+Dvazf2yIvyCkOg3Ae7L/5k1Odd5FcPnSXt82FBtJpYo7WlUulw4EAtJkxw0h9jAYwb54Rhw2WtXlNXp8MTq4vg7s7H+xv9sWy5B77+WoFdX1frzykqUuOZp4vRp48EH27yx5y7XfD+e+U4evTGasuLFxqx7qVSjB4jw6aPAjB6jAwvvViC9Jv2yAsNE8PHR4BDh2xrlWbv6rfxGv8lxDpyHckN7kIBngn15ToMYkaURHFMIBRi5MKHuA4DADAk8W4MlN5hc7WfjmSfAJ/HN9jP7sXRj2B+/HQEubT+B2/3xZ+g0jThzQlPIdIzFON7DcOylLn4+M+v9L0F2898C38nL7wwegV6evTA3X0n4a7YCdh009Dc5lNfY0iPRCxLmYtw92AsS5mLQcEJ2Hzqa4P7jQkfhG8v/mzU552LEqO2Z0vy8ow3X+WPPxrA5wPRN+2Bt2yZB6ZMlcPXV9DqNYcOKdHUxOLx1V4ICRFhyBAp7p7jil27qvWvr33f1cDLS4ClD3sgOFiECROdMW6cE77+6kai9c1/q5GQ4IA5c1wRFCTCnDmu6BfvgP9+U21wv5SBUhz+xbaSKACQq/7GY/X3YJ680CJqSj0f5gcXYes/c2KbKImyAD1i+yEiZQhn95fJXDFj0BPwqwgCbLD208mCs4j16dyedH8VXkByYF+IBTfmrA0LSUKJslw/r+p04QUMDelvcN3QkCScK86AWqvRt3PrOcNCknC68B+DY3G+UThblAGVpqlTcbZFI2KRU0pDeW3JyWEhEvkYpa3z5xoQEdG5ntuLFxsR21cCkejGW39iogMqKrQoLtb87xwVEhIcDK5L7O+Iy5dV0GhYfTsJibeck+iACxdUBscie4mRkaFCU5PtLTLgs40Yp1iBl6W7OK0pNUAuxV2+bpzdn3CDkigLMeLe+yFycGj/RCPrFT4Qk8KWQmDDBa0LaorhLetcz0NpXQU8pYZ/ED0cm78vq6vU/9fjlnM8HV2h0WlR2aC46RzDicweUld9G9f5OHlCpW1qcbyrivwboNFQOYrb0WnjjNJOcbEG7u6d632oqtTC1dVwQ9rr31dVNs9DrGzjHK22eSL7jXYEt5wjQFWV4c/ew4MPtZptcdyWBCu/wCu6RzCEg5pSQobBq70CzH5fwj1KoiyEzM0dg2bNNdv9GIaH1IEPII4dYvO1nxrVKoMepY66dXiABfu/48xN5zC3nNPy+K3ngGVbHJP8L74GdSOMIZdXapR2bFlBgadR2lE1sQY9Sh3V4gq2lQdavnSaD990vMUiMJZtcaFI3PynvrHR9nqibuagyceSmruxTP6PWWtKPRDoiUip+T8EE+5REmVB4sZNglcP01cyd3cPxMzkJ+FS5ArY3uhdC26OclQ3dm4+iJfUHaW39ApV1FcBgL5nyVPqhrK6CoNzyuurIODx4eogv+mcylvOUbTonVI01gIA3B1dOhVna7RCFtml+d1ux9ZlZrIQCrufSMnlfNQqO7eK1dWNj8oqw2uqFM3fX+99cnPj63ulrlMotODzAWdn/o12KjUt2rm1B6u2trkdFxfD47aIAYsUxRqsF7+PEDPUlPIXC7Gqh7fJ70MsEyVRFoTH42P0/UvBMKb7sSTETsQY73k2Ufupo2K8euJKRW6nron3j8EfBWfRpL0xNHA05094yzz0ZRIS/GPwW+4pg+uO5vyJWJ9ICPkCfTu/5f7Z4pybJ7kDwKWybPg6ecLNCElUiZ8KarVtLQ4wDQZAXLdbCQ8XIT+vc//e0dESnD/XCLX6Rs/Q6VMNcHfnw8dH8L9zxDh9usHgulOn6hERIYZAwOjb+euWc06fakBMjOEcrdycJnh68iGX234SdZ1XwyGsUS3EHc7V7Z/cDS/3DICUbz//rsQQJVEWxrdnL/QZNdbo7YqEDrhj8EqE1/a2mdpPHTUsNAmXy3P0vT0AkFN1FRdKrqCsrhKNGhUulFzBhZIr+qRpavRoiAQirNr/CjLKsnHg8lG8d3w7FvefBeZ/wwRz46bgak0J1h56D1fKc7Hz3H58eW4/liTdpb/PooQ7cTTnFDae2IHMijxsPLEDaXmncH/iTIMYT149h6E9DCegd1WO0DhDeXl5efjiiy+wYcMGrF27FhkZGQaPsyyLI0eOYMOGDXj55ZexdetWlJa2f++LFy/i/fffx7p16/D+++8jPT3d4PFz587hrbfewvr16/Hjjz8aPKZQKPDuu+9CpTKcON1V1wq7P7k8MdERublN+t4eACgsVCMzU4WqSi1UKhaZmSpkZqr0SdPIkTIIhQxee60UOTlNSEurw+dfVOHOO+X619ekyc4oLdXgg40VyMtrwoEDNTh4oBYzZ8n195k+XY5Tpxqw8wsF8vObsPMLBf76qwHTZ8gNYjx/vhEJCRZUC8BMhLoq3FW9EM84HYGTCTYCHuvujHGe8vZPJDaLYWlPCIvTqFTi01UPor5aYZT2ggJ6Y6D3VLCV9ts7MeWzhzCzzzjMjZsCAJj5+QqcKDjT4rxjD36p72lKL8vCsz++hbNFGZBLZJgbNwUrB83Xv8kBzcU2X/zlXVwuz4W3zB0PJd+Def2mGLS5P+MIXv9tM/IV1xDs4ofVQxdjfK9h+scbNSrEvzsV22e9gXj/mG49Ty2fxeey342SZFy5cgUFBQXw9fXFV199hbvuuguRkTdWOaalpeG3337D1KlT4e7ujqNHjyIvLw/Lli2DWNz6arWCggJ8+umnGDFiBKKiopCeno4jR45gwYIFCAgIQH19Pd566y1MmTIFrq6u+PzzzzFlyhREREQAAHbs2IH4+HhERUV1+/kBAMOwGD5iPzSaqm61s3xZIVJTnTBpsjMAYNWqazh3tuX8tu07AvUFObOzm/DuO+XIyFDByYmHSZOdMW+ei8Hr6+zZBn0S5e4uwF2zXTD5f/e47uivSnz6aRWKitTw8xNiwUI3DBlyo9hjU5MOd96Zh1df9UV0tAT2qlrcF5sEz+NsvXHac+DxcDQ5EoESy911gpgeJVEW6sKvh3Bw41vdbmdo/znwrQ62ydIFnfFL1nGsO7wRPy/aBp4Jh0u7Yutf/8WPV9Lw+V1vdrut4qBG7Cv93QhRGVq7dq1BEsWyLN58800kJydj8ODBAACNRoM33ngDo0ePRmJiYqvt7Nq1CyqVCvfcc2MD7u3bt8PBwQEzZsxAYWEhvvjiCzz22GP68319fTFo0CCcP38eFy5cwOzZs4363EaNLkRT0y/dauPkyXps+rACm7cEgMezhIpFN3y7pxrHjtVj/WtUBFLHiPCz/HV8Vh3Q7emgT4f6YkUwzYWyd5b1bkL0YoaNQkBU1zdKdZK5Y8agJ+BbHmj3CRQAjAxLwT1xd6C4tozrUFoQ8gR4afRKo7SVJ6po/yQjUCgUUCqVCAu7sRBCIBCgR48euHq19W1OgOaeqNDQUINjYWFhKChormnl5uYGtVqNoqIiNDQ0oLCwEN7e3mhoaMDhw4cxfvx4oz+X4qLu7xiQnOyISZOcUV5ueUPlfAGDZctpM1wA4LFNGKt4BOukX8Jb1PW3v56OYjwU6GXEyIi1otKqFmz04oex/cmV0DR1bmgmKmIw+kqGgb1m26ULOmvRLfOQLMU9cXcYpR0dj0VmRZ5R2mqPUtm82lEmM9zWRCqVorq67Ym8SqWyxTUymUzfnoODA6ZOnYo9e/ZArVajb9++CA8Px7fffoukpCQoFArs3LkTWq0Ww4cPR3R0dLefS0YGgyFDnaHVdm9T21vnIVmKSZOc2z/JzgQrv8LLghP4zGk9fq3t3HAcD8AbvQIhtLAeR8INSqIsmLt/IIbOXYBfPvmwQ+fzeHyMGXA/XEpcwNZSAmVvyv3UaChvaP9EE+rK7IBbr4mKijKY85Sbm4vS0lJMmDAB77zzDmbMmAGZTIbNmzcjODgYUmn3NnvVagEBvx+02l+71Q6xLg6afDxQczdiXV7ARzWxUHXwtbs82BvJLq3viUjsDw3nWbh+qZMQ0q/1+SU38/AIwsykJ+FS5GIXtZ9IS3kO5Wa71/XepOs9SNfV19e36Gm69bpbr6mrq2vzGo1Gg/3792PSpEmorKyETqdDjx494OHhAXd399sOHXZGSSlVm7ZXAxQvYL34XYR1oKZUXycHPNbDONsFEdtASZQVSH3wETg4tz1UkBg7CaO95gIl1Ptkr1iGRWaF+Qpsuri4QCaTITs7W39Mq9UiNzcXAQFtJySBgYEG1wBAdnY2AgMDWz3/6NGjCA8Ph6+vL3Q6HXS6G58QtFptl3q+WpORzgOfZ38lAEgzz4bDeK5xPqbIFW2e48jnYWN0MA3jEQOURFkBqYsrUh98pMXx5tpPjyKsNsbuaj8RQxW+atTV1xm1zaamJhQXF6O4uBgAUFVVheLiYlRXV4NhGCQnJ+O3335Deno6SktLsWfPHgiFQvTp00ffxu7du/Hzzz/rv09OTkZWVhbS0tJQXl6OtLQ0ZGdnIzk5ucX9S0tLceHCBYwYMQIA4OHhAYZh8Ndff+Hy5csoLy+Hn1/3J4UDgFoNCIT9jNIWsU5CthqzFIvwrNMvcG6lptTacD+EOdpviQjSOipxYEV+3vw+zv50AADVfiKG/upZgr8K/jFqm7m5udi2bVuL43379sXUqVPBsix+/fVXnD59Gg0NDQgICMCECRPg5XVj1dLWrVvh4uKCqVOn6o9dvHgRv/zyC6qqquDm5oaRI0e2qPvEsiw+/fRTDB48WF8jCgAuX76M77//HhqNBiNHjkR8fLzRnm9cnBZOzp8brT1ivWrEsfhI+Dz+rmvudRrn4YytfULbuYrYI0qirIha1YjtT65En4Bh8FEEUekCAqB5KO9rj1Ooqe3e6jJ7JxYDA1K+hk5nnE2giXXTMSIckq/HwYZQHOofCXcRrcMiLdFwnhURiiW467F/waeKaj+RG6q8tZRAGYFKBYiEcVyHQSwEj23CGMUq7I9qogSKtImSKCvj6O8GlwnUrUxuyHeq5DoEm1FZ2YPrEIgFCQ5+EP5uxhsyJraHkigrJBvoB4feVIGYNMusLuA6BJuRni4Cwwi5DoNYABd5f4SFPsp1GMTCURJlpVzvjADfjVaK2LtqTw0UNQquw7AZ9fUsxOK+XIdBOCYUuiGm97/BMHyuQyEWjpIoK8WTCOA+JxLgU80Se5bvouA6BJujqApr/yRiwxhER78OiZiKapL2URJlxUQBTpCPD+E6DMKhLKVxKnbfqr6+Hq+//joUCoVJ2u+Or776CsePHzdZ++npYjAMTSS2V8FBi+HhPpzrMIiVoL8UVs5psD9UOdVovFDBdSjEzGrcNSivMs3PPS0tDREREXBxcQEAHDhwAAUFBSgtLYWHhwcefPDBFteUlJTgwIEDKCwshIODAxISEjB06FAwzI3e0tzcXPz4448oLS2Fk5MTBg0ahMREw22NLl68iMOHD6Oqqgqurq4t6kgNGzYM27ZtQ3x8PMRisdGfu1LJQizug8bGv43eNrFscnkCQkP/j+swiBWhnigb4DYzAgJv2rLC3hS4maasgVqtxt9//92ikGVcXBxiYmJavUalUuGzzz6Dk5MTFi9ejPHjx+PYsWMGPUZVVVX4/PPPERQUhCVLlmDIkCE4cOAALl68eOM5FRRg165diI2NxYMPPojY2Fjs2rXLYI88b29vuLi44Ny5c0Z+5jfU1ISbrG1imcRiH/Tp/S54POpbIB1HSZQN4EkE8JgfA56MVhXZk6x60wzlZWZmgsfjGexnN378eCQlJcHV1bXVa86dOweNRoMpU6bAy8sLUVFRGDJkCE6cOKHf3+7UqVOQy+UYN24cPD09ER8fj379+hkkWidPnkRYWBiGDBkCDw8PDBkyBCEhITh58qTB/SIiIvDPP8at0H6zSxmOoD+P9oPPl6Jv7GaIxd5ch0KsDP2VsBECVwnc740GI6QfqT1QumpRWlFmkrbz8vI6vSfd1atX0aNHDwgENz7Fh4WFoba2Vj+v6urVqwgNNaxxFhYWhmvXrkGrbd77saCgoNVzCgoMyzj4+/ujsLAQGo1pNt1WKHSQSFrvdSO2hWH46B3zNpycoto/mZBb0DuuDREHOcN1VgRAC/Zs3lWPWpO1rVAoIJPJOnWNUqmEVCo1OHa9DaVSqf/vre3KZDLodDrU19ff9pzrbVzn7OwMrVbb4rgxKZUR7Z9ErF7P8Gfg4TGC6zCIlaIkysY49vGE89geXIdBTCy7sdBkbWs0GoMepa66Pox388Ty7pxzs+vxqdWm24D78iUp6BOJbQsImIfAwPu4DoNYMUqibJDziEA4JtDYvq2ql+twrazYZO07OjqisbFzm/DKZDLU1dUZHLv+/fUeqtZ6lOrq6sDj8eDg4HDbc27tnWpoaDBo2xQqKlhIJJEma59wy919OCJ6Psd1GMTKURJlo1ynh0McJuc6DGICVz1NN5QHAD4+Pigr69x8q4CAAOTl5ennNgFAVlYWnJyc9GUSAgICkJ2dbXBdVlYW/Pz8wOc3V4YODAxscU52drbBJHcAKC0thbOzMxwdTbsqtb6ekihbJJNFoXfMO1SRnHQbJVE2iuHz4D43GgJPB65DIUaW3XTNpO2HhYWhrKxM39sDAJWVlSguLoZSqYRGo0FxcTGKi4v1SVOfPn3A5/OxZ88elJaWIj09HWlpaRgwYIB+qC4xMRHV1dX44YcfUFZWhr///ht///03UlJS9PdJTk5GVlYW0tLSUF5ejrS0NGRnZyM5Odkgxvz8/BYT0E0h84qTye9BzEsk8kLf2I8hEJiuF5PYD4ZtbcIBsRmaigaUbjwDXZ1pVjER82qU6bBDe6TVeULGtGXLFvTt21dfCHPr1q3Iy8trcd4jjzyi72kqKSnB999/b1Bsc9iwYS2KbV5Pom5XbPOXX35BVVUV3NzcWhTb1Gg0eOONNzB37lwEBASY4NkbGjPmBBpVV0x+H2J6PJ4DEuK/gLNzH65DITaCkig7oMqtRtnm84CGftTWLiu8Foev/mHy+1y5cgU//vgjli5dettJ31z4448/cOnSJcybN88s9xsytAbAt2a5FzElHmL7bISn5xiuAyE2hIbz7IC4hxxud9JybVuQrSkyy3169uyJhIQE1NSYpip6d/D5fIwfP95s98vKpLmFtiA8/AlKoIjRUU+UHan5OQ81P+dzHQbpoiZHHbbjV+h0Oq5DsTujx/wGlSqX6zBIF/n73Y3IyHVch0FsEPVE2RHn0cFw7OfFdRikiwp96imB4khTU2+uQyBd5OY2BBERL3AdBrFRlETZGdc7I+AQ4851GKQLcljT1YYit5eT48Z1CKQLXFySENtnI20qTEyGkig7w/AZuM2JokTKyqglOuSVmmbDYdK+gnwWIpHpVwIS43FxSUJc3y3g801bS4zYN0qi7BAlUtanyK/BoJAlMT+tJpbrEEgHUQJFzIWSKDtFiZR1yUEJ1yHYvfx8D65DIB1ACRQxJ0qi7BglUtZBI2KRU1rAdRh2LztbB5HQh+swyG1QAkXMjZIoO0eJlOUr9m+ARkMV57nHQKfry3UQpA2UQBEuUBJFKJGycDm8zm0GTEzn6lVvrkMgraAEinCFkigCgBIpS6URsMgpowKpliIzk4VQSHOjLAklUIRLlEQRPUqkLE+pvwpNTU1ch0H+p3l/h35ch0H+hxIowjVKoogBSqQsS66QhvIszbVCGtKzBJRAEUtASRRpgRIpy6Dls8gqy+M6DHKLy5cZCASuXIdh1yiBIpaCkijSKn0i1ZsSKa6U+6ugUqm4DoPcQqcDeDwa0uOKi0syJVDEYlASRdp0PZGSDfTjOhS7lCuu4DoE0obiIvqd4IK31yTE9f2UEihiMSiJIrfF8Bi43BEGl8mh9GoxIx2PRVY5rcqzVJcuMeDznbgOw670CF6KmJh/g88Xcx0KIXr0tkg6RDbIH+73xoAR8bkOxS5U+DWhvqGe6zBIGzQaQMCP5zoMu8AwIkRHvY6wsP8DwzBch0OIAUqiSIc5RLrB88FY8OUirkOxebkOlVyHQNpRWhrAdQg2TyBwQb+4rfD1nc51KIS0ipIo0ikiPxm8Ho6D0F/GdSg2i2VYZFXSqjxLl57OA59Hc3NMxcEhGP0Td8HVNZnrUAhpEyVRpNP4zmJ4LomFJJpW7plCpa8ayro6rsMg7VCrAYGQVumZgou8P/onfgNHxxCuQyHktiiJIl3CE/HhPjcKssH+XIdic/KkVVyHQDqoojyY6xBsjo/3VPTr9x8IhVSLi1g+SqJIlzE8Bi6TQuEyNRzg0YRPY8lU0Ko8a5GeLgCPR6vFjCUkZCViYjaAx6N5l8Q6UBJFuk02wBce82PAiGnlXndVeatRU1vDdRikgxobWYhENKTXXTyeCDHRbyE0ZDnXoRDSKZREEaOQRLjC66G+4LvQp/LuyHOmoTxrU1nRg+sQrJpQ6IZ+cZ/Bx+cOrkMhpNMoiSJGI/SRNq/cC6QihF2VVVPAdQikkzIyRGAYIddhWCVHx1AkJuyCi0si16EQ0iWURBGj4juJ4LUkFtIUX65DsTrVnhpUVSu4DoN0Ul0dC7E4juswrI6392T0T9wNR0eanE+sFyVRxOgYAQ+uU8Lhdk8UGAnNk+qofJdqrkMgXaRQ0FL8juLxxIjs9TJ6x/wbAgHVmyPWjZIoYjKOfTzgvSIewgD6Q9kRWUoayrNWGRkSMKAPDO1xdAxF/8Td8PefzXUohBgFJVHEpARuEng92BeyQbTr/e3UumtQXlXBdRiki2prWIglfbgOw6L5+ExFUv9vIZP14joUQoyGkihicoyAB5fJYXCfFwXGQcB1OBapwI3KGli7mppwrkOwSDyeA6IiX0VM9Abw+bRNDrEtlEQRs3GI8YD3I/EQh8q5DsXiZNUXch0C6aZLGVLQn1RDTk4xSOr/Lfz8ZnIdCiEmQb/xxKwELmJ43N8H8vE9AD5VOQeAOhcdSipKuQ6DdJNCoYNEEsN1GBaCh+CgJUhM+AZSaRjXwRBiMpREEbNjeAychgXCa2kcBF4OXIfDuQJPGsqzFXXKCK5D4JxY7Iv4ftsRHr4aPB7VzyK2jZIowhmRvwzey/vZfU2p7EYayrMVly/b90pUb+/JSE76Hq6uyVyHQohZUBJFOMUI+XCdEg73BTHgOdnfp9YGZx2ulRVzHQYxkvJyHSSSSK7DMDuBwAkx0W+id8y/IRQ6cx0OIWZDSRSxCA693OCzKhHSAb6AHU2VuupVy3UIxMjq66O4DsGsfLynYMCAn+HjM4XrUAgxO1pvTiwGz0EA16nhkCZ6o2p3JtSFSq5DMrnspiKuQyBGlnnFCRF2UArJ0TEUvSLWws1tINehEMIZhmVZlusgCLkVq2NRd7II1T/kgm3Uch2OSTRKddihOwL6FbQ9Y8YcR6Mqk+swTILHk6BHj6UIDloMHk/EdTiEcIp6oohFYngMZCl+cOjtAcX+bDScKeM6JKMr9FGCLaQEyhY1qmIA2F4S5e4+HL0iXoCDQyDXoRBiESiJIhaN7ySC++xINCZ6Q/FtFjRlDVyHZDQ5WppQbquys1wQakPlkcRiH0T0fB5eXqlch0KIRaHhPGI1WI0OtUevovZwAVi1jutwuqXJQYftzK/Q6az7eZC2jR7zG1SqXK7D6BaGESAwcD5CeqyAQCDlOhxCLA6tzrMDDMNgz549bT5+5MgRMAwDhUJhtpi6ghHw4DwyCN6PJkDSy5XrcLql0K+eEigb19TUm+sQukUuT0BS/73oGf4UJVCEtIGSKBtQWlqKJUuWICgoCGKxGD4+PkhNTcXx48c7dP3AgQNRVFQEufz2e9rNnz8fU6dONULE3SNwk8BjQW+4z40CX26dE1tzdCVch0BMLDfXjesQukQodEVU5KtIiP8SMhm3ywznz58PhmH0X+7u7hg3bhzOnTvHaVyEXEdJlA2YMWMGzp49i23btuHy5cvYu3cvhg8fjsrKyg5dLxKJ4OPjA4ZpvUCTVqu1yF4Th94e8F6VCNlQf0BgPcWl1BId8koLuA6DmFh+HguRyJ/rMDqMYfjw87sLKQN+gp/fzDb/HpjbuHHjUFRUhKKiIhw6dAgCgQCTJk3iOixCAFASZfUUCgXS0tKwfv16jBgxAsHBwUhKSsJTTz2FiRMn6s8rLy/HtGnT4OjoiJ49e2Lv3r36x24dztu6dStcXFywb98+REdHQywWY8GCBdi2bRu+/fZb/afCI0eOmPnZtsQT8+EyIRS+j/dvLtRpBZsaF/k2QKu1zbINxJBW25frEDqABx/vqRiQ/AOiIv8FodCyhsqv9677+PggLi4OTzzxBAoKClBW1rxi94knnkBERAQcHR0RGhqK5557Dmq12qCNdevWwcvLC05OTrj//vvx5JNPIi4ujoNnQ2wNrc6zcjKZDDKZDHv27MGAAQMgFotbPW/t2rV47bXX8Prrr+Pdd9/FPffcg7y8PLi5tT7kUF9fj1deeQWbN2+Gu7s7fHx80NjYiJqaGnz66acA0Oa1XODLxXCdGg6nYQGo/aUAdadLAJ1lrpnIYUq5DoGYSX6eB/wDuI6iLQy8vCYgNOQRSKXWsZRQqVRix44dCA8Ph7u7OwDAyckJW7duhZ+fH86fP4/FixfDyckJq1evBgDs2LEDL7/8MjZu3IhBgwZh586d2LBhA0JCQrh8KsRG0Oo8G/DNN99g8eLFaGhoQHx8PIYNG4bZs2cjNjYWQPPE8meffRYvvfQSAKCurg5OTk74/vvvMW7cOBw5cgQjRoxAVVUVXFxcsHXrVixYsABnzpxB3743PknPnz8fCoXitpPULYWmogE1h/JRf6YUsKCRSI2IxXbRUWg0Gq5DIWbBYtSon9GktqRyFgw8PcciNOQRzuc8tWf+/PnYvn07JBIJgOa/Xb6+vti3bx/i4+Nbveb111/Hl19+iVOnTgEABgwYgMTERLz33nv6cwYPHgylUokzZ86Y/DkQ20bDeTZgxowZuHbtGvbu3YvU1FQcOXIE8fHx2Lp1q/6c6wkVAEilUjg5OaG0tO0eEZFIZHCNtRG4O8BtVi94P5oAhzhPi9mPr9ivkRIou8JAx8ZxHYSeh8coJPX/FrF9Nlp8AnXdiBEjcObMGZw5cwYnT57E2LFjMX78eOTl5QEAdu3ahcGDB8PHxwcymQzPPfcc8vPz9ddfunQJSUlJBm3e+j0hXUVJlI2QSCQYM2YMnn/+eRw7dgzz58/HmjVr9I8LhUKD8xmGue1kcQcHB4uZWNodQk9HuM+OhPfKeDj08eA8mcrl01Cevbla4Ml1CHB3G4r+ibvRN/YjODnFcB1Op0ilUoSHhyM8PBxJSUnYsmUL6urq8PHHH+PEiROYPXs2xo8fj3379uHvv//GM888g6amJoM2bv1bRgMwxFgoibJR0dHRqKurM2qbIpHIaidEC72lcL8nCl4r4iGJduckBo2ARXZZfvsnEpuSmQkIhdy85lxdByIh4SvExX0KZ2fr7Vm+GcMw4PF4aGhowO+//47g4GA888wzSExMRM+ePfU9VNf16tULf/zxh8Gx60N9hHQXTSy3chUVFZg5cyYWLlyI2NhYODk54dSpU3jttdcwZcoUo96rR48e+OGHH3Dp0iW4u7tDLpe36OGydCJfKTzujUZToRI1P+WhMaNjZSCMocxPhabSpvZPJDaludOjH4CfzXZPF5ckhIashKtrstnuaSoqlQrFxc1zyqqqqvDee+9BqVRi8uTJqK6uRn5+Pnbu3In+/ftj//792L17t8H1y5cvx+LFi5GYmIiBAwfiyy+/xLlz5xAaGsrF0yE2hpIoKyeTyZCcnIy33noLWVlZUKvVCAwMxOLFi/H0008b9V6LFy/GkSNHkJiYCKVSicOHD2P48OFGvYe5iPxl8Jgfg6aCWtSmFaLhn3JAa9ou/hyR7W2iTDrmWqEPPL1Mew+G4cPdfQQCA+6Dm9tA097MjA4ePAhfX18AzSvxIiMj8fXXX+v/9jz66KNYtmwZVCoVJk6ciOeeew4vvPCC/vp77rkH2dnZeOyxx9DY2IhZs2Zh/vz5LXqnCOkKWp1HCABtbRPq/ixG3R/F0CpURm9fx2fxuewYGlWNRm+bWD4eDxg2/DtoNAqjty0UusPfbxb8/edAIvEzevu2aMyYMfDx8cFnn33GdSjEylFPFCEA+E4iOI8MgtPwQDRmVEJ5ogiqK1WAkT5ilPo1obGMEih7pdMBPF4/AIeN1qZcnogA/3vg5TUOPJ51bn9kDvX19fjwww+RmpoKPp+PL774Aj///DN++uknrkMjNoCSKEJuwvAYOES7wyHaHZryBihPFqH+dAl09d0rS5AvKTdShMRaFRf7o7v1afl8KXx8piDAf67VlCjgGsMw+P7777Fu3TqoVCr06tUL33zzDUaPHs11aMQG0HAeIe1g1TrUny2D8sQ1qK8qO329jsdip/wE6hvqTRAdsRYCATB4yB5otbWdvlYq7Ql//3vg6zMNAoHMBNERQrqCeqIIaQcj5EGa6A1pojeaCmqhPFGEhnNlYNUdK4Ve4atGfQUlUPZOowEEgn7Qao926HyGEcLTcwwC/OfaxCo7QmwRJVGEdIIo0AlugU7QTQxB3ekS1P1RDE1Zw22vyXOsACrMFCCxaKUlAZC73P4cidgPfn6z4Oc3G2Ix94U6CSFto+E8QrqpqVCJhnNlqD9fDm2l4eRxlmHxpdsfUNZ1fhiQ2B6RCEgZ+A10uvpbjnvBy2s8vL0nQu4cbxO7BRBiD6gnipBuEvnLIPKXQT4+BE0Ftag/W4aG82XQVjeh0kcDZRUlUKRZUxMgEvZDo+p3CIXuzYmT1wS4uPQHw9AGEoRYG+qJIsQEWJZFU34tLmVfxqEzv6G6uprrkIgFkMlkGDo0COHh/nB1HQCG4XMdEiGkGyiJIsQMioqKkJGRgUuXLum3sCD2wcPDA7169UJkZCQCAgJoqI4QG0JJFCFmplAokJGRgYyMDOTn50On69gqP2IdGIaBv78/IiMjERkZCQ8PD65DIoSYCCVRhHCooaEBBQUFyMvLQ15eHq5du0ZJlZVhGAY+Pj4IDg5GcHAwgoKCIJVKuQ6LEGIGlEQRYkHUajWuXr2qT6quXr0KtVrNdVjkJnw+H/7+/vqkKTAwEGKxmOuwCCEcoCSKEAum1WpRVFSEvLw85OfnIz8/Hw0Nt69LRYxLLBYjMDAQQUFBCA4Ohr+/PwQCWthMCKEkihCrwrIsSktLkZ+fr++tqq3t/DYipG2Ojo76Ybng4GD4+PiAx6PyA4SQliiJIsTKKZVKlJWVoby8XP9VVlaGmpoarkOzaDKZDB4eHvD09ISHh4f+/52dnbkOjRBiJSiJIsRGqVQqVFRUGCRYZWVlqKystJvJ6wzDwNXVtUWi5OHhAYlEwnV4hBArR0kUIXZGq9WiqqpKn1RVVFSgtrYW9fX1+i9rmcwuEAjg6Oio/5LJZHB3d9cnSm5ubjR/iRBiMpREEUJaUKvVqK+vR11dnUFy1dZXQ0MDtFptt+7J4/Hg4OBgkBS19SWVSuHo6AiRSGSkZ0wIIZ1HSRQhxGh0Op3+i2VZg/9nWRY8Hk//xTCMwfeEEGJtKIkihBBCCOkC+vhHCCGEENIFlEQRQgghhHQBJVGEEEIIIV1ASRQhhBBCSBdQEkUIIYQQ0gWURBFCCCGEdAElUYQQQgghXUBJFCGEEEJIF1ASRQghhBDSBZREEUIIIYR0ASVRhBBCCCFdQEkUIYQQQkgXUBJFCCGEENIFlEQRQuxObm4uGIbBmTNnuA6FEGLFKIkihJhVaWkplixZgqCgIIjFYvj4+CA1NRXHjx/nOjRCCOkUAdcBEELsy4wZM6BWq7Ft2zaEhoaipKQEhw4dQmVlJdehdYtarYZQKOQ6DEKIGVFPFCHEbBQKBdLS0rB+/XqMGDECwcHBSEpKwlNPPYWJEycCABiGwebNmzFt2jQ4OjqiZ8+e2Lt3r0E7Fy9exIQJEyCTyeDt7Y158+ahvLxc//jBgwcxePBguLi4wN3dHZMmTUJWVlabcel0OixevBgRERHIy8sDAHz33XdISEiARCJBaGgo1q5dC41Go7+GYRh8+OGHmDJlCqRSKdatW2fMfypCiBWgJIoQYjYymQwymQx79uyBSqVq87y1a9di1qxZOHfuHCZMmIB77rlH31NVVFSEYcOGIS4uDqdOncLBgwdRUlKCWbNm6a+vq6vDqlWr8Oeff+LQoUPg8XiYNm0adDpdi3s1NTVh1qxZOHXqFNLS0hAcHIwffvgBc+fOxYoVK3Dx4kVs2rQJW7duxcsvv2xw7Zo1azBlyhScP38eCxcuNNK/EiHEarCEEGJGu3btYl1dXVmJRMIOHDiQfeqpp9izZ8/qHwfAPvvss/rvlUolyzAMe+DAAZZlWfa5555jx44da9BmQUEBC4C9dOlSq/csLS1lAbDnz59nWZZlc3JyWADsb7/9xo4ePZodNGgQq1Ao9OcPGTKE/de//mXQxmeffcb6+voaxLly5cou/isQQmwB9UQRQsxqxowZuHbtGvbu3YvU1FQcOXIE8fHx2Lp1q/6c2NhY/f9LpVI4OTmhtLQUAHD69GkcPnxY36slk8kQGRkJAPohu6ysLMyZMwehoaFwdnZGSEgIACA/P98glrvvvhtKpRI//vgj5HK5/vjp06fx4osvGtxj8eLFKCoqQn19vf68xMRE4/7jEEKsCk0sJ4SYnUQiwZgxYzBmzBg8//zzuP/++7FmzRrMnz8fAFpM0GYYRj8Up9PpMHnyZKxfv75Fu76+vgCAyZMnIzAwEB9//DH8/Pyg0+nQu3dvNDU1GZw/YcIEbN++HSdOnMDIkSP1x3U6HdauXYvp06e3Gvt1Uqm0a/8AhBCbQEkUIYRz0dHR2LNnT4fOjY+PxzfffIMePXpAIGj5J6yiogLp6enYtGkThgwZAgBIS0trta2HHnoIvXv3xh133IH9+/dj2LBh+ntcunQJ4eHhXXtChBC7QEkUIcRsKioqMHPmTCxcuBCxsbFwcnLCqVOn8Nprr2HKlCkdauPhhx/Gxx9/jLvvvhuPP/44PDw8kJmZiZ07d+Ljjz+Gq6sr3N3d8dFHH8HX1xf5+fl48skn22xv+fLl0Gq1mDRpEg4cOIDBgwfj+eefx6RJkxAYGIiZM2eCx+Ph3LlzOH/+PK3CI4ToURJFCDEbmUyG5ORkvPXWW8jKyoJarUZgYCAWL16Mp59+ukNt+Pn54ffff8cTTzyB1NRUqFQqBAcHY9y4ceDxeGAYBjt37sSKFSvQu3dv9OrVC++88w6GDx/eZpsrV66ETqfDhAkTcPDgQaSmpmLfvn148cUX8dprr0EoFCIyMhL333+/kf4lCCG2gGFZluU6CEIIIYQQa0Or8wghhBBCuoCSKEIIIYSQLqAkihBCCCGkCyiJIoQQQgjpAkqiCCGEEEK6gJIoQgghhJAuoCSKEEIIIaQLKIkihBBCCOkCSqIIIYQQQrqAkihCCCGEkC6gJIoQQgghpAsoiSKEEEII6QJKogghhBBCuoCSKEIIIYSQLqAkihBCCCGkCyiJIoQQQgjpAkqiCCGEEEK6gJIoQgghhJAuoCSKEEIIIaQLKIkihBBCCOkCSqIIIYQQQrqAkihCCCGEkC6gJIoQQgghpAsoiSKEEEII6QJKogghhBBCuoCSKEIIIYSQLqAkihBCCCGkCyiJIoQQQgjpAkqiCCGEEEK6gJIoQgghhJAuoCSKEEIIIaQLKIkihBBCCOkCSqIIIYQQQrqAkihCCCGEkC6gJIoQQgghpAsoiSKEEEII6QJKogghhBBCuuD/AYf3XwY2cet8AAAAAElFTkSuQmCC",
+ "text/plain": [
+ "<Figure size 1000x700 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "def func(pct, allvalues):\n",
+ " absolute = int(pct / 100.*np.sum(allvalues))\n",
+ " return \"{:.1f}%\\n({:d})\".format(pct, absolute)\n",
+ "\n",
+ "def plot_pie(title, data):\n",
+ " # Creating plot\n",
+ " fig, ax = plt.subplots(figsize =(10, 7))\n",
+ " plt.pie(data, autopct = lambda pct: func(pct, data), labels = class_names)\n",
+ " ax.set_title(title)\n",
+ " \n",
+ " # show plot\n",
+ " plt.show()\n",
+ "\n",
+ "plot_pie(\"Train data distribution\", df_train[\"label\"].value_counts())\n",
+ "plot_pie(\"Validation data distribution\", df_val[\"label\"].value_counts())\n",
+ "plot_pie(\"Test data distribution\", df_test[\"label\"].value_counts())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
"metadata": {},
- "outputs": [],
"source": [
- "raw_data = raw_data.drop(['year'], axis=1)"
+ "Ein einziges Bild ausgeben"
]
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>valence</th>\n",
- " <th>acousticness</th>\n",
- " <th>danceability</th>\n",
- " <th>duration_ms</th>\n",
- " <th>explicit</th>\n",
- " <th>instrumentalness</th>\n",
- " <th>key</th>\n",
- " <th>liveness</th>\n",
- " <th>loudness</th>\n",
- " <th>mode</th>\n",
- " <th>popularity</th>\n",
- " <th>speechiness</th>\n",
- " <th>tempo</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>0.0594</td>\n",
- " <td>0.982</td>\n",
- " <td>0.279</td>\n",
- " <td>831667</td>\n",
- " <td>0</td>\n",
- " <td>0.878000</td>\n",
- " <td>10</td>\n",
- " <td>0.665</td>\n",
- " <td>-20.096</td>\n",
- " <td>1</td>\n",
- " <td>4</td>\n",
- " <td>0.0366</td>\n",
- " <td>80.954</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>0.9630</td>\n",
- " <td>0.732</td>\n",
- " <td>0.819</td>\n",
- " <td>180533</td>\n",
- " <td>0</td>\n",
- " <td>0.000000</td>\n",
- " <td>7</td>\n",
- " <td>0.160</td>\n",
- " <td>-12.441</td>\n",
- " <td>1</td>\n",
- " <td>5</td>\n",
- " <td>0.4150</td>\n",
- " <td>60.936</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>0.0394</td>\n",
- " <td>0.961</td>\n",
- " <td>0.328</td>\n",
- " <td>500062</td>\n",
- " <td>0</td>\n",
- " <td>0.913000</td>\n",
- " <td>3</td>\n",
- " <td>0.101</td>\n",
- " <td>-14.850</td>\n",
- " <td>1</td>\n",
- " <td>5</td>\n",
- " <td>0.0339</td>\n",
- " <td>110.339</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>0.1650</td>\n",
- " <td>0.967</td>\n",
- " <td>0.275</td>\n",
- " <td>210000</td>\n",
- " <td>0</td>\n",
- " <td>0.000028</td>\n",
- " <td>5</td>\n",
- " <td>0.381</td>\n",
- " <td>-9.316</td>\n",
- " <td>1</td>\n",
- " <td>3</td>\n",
- " <td>0.0354</td>\n",
- " <td>100.109</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>0.2530</td>\n",
- " <td>0.957</td>\n",
- " <td>0.418</td>\n",
- " <td>166693</td>\n",
- " <td>0</td>\n",
- " <td>0.000002</td>\n",
- " <td>3</td>\n",
- " <td>0.229</td>\n",
- " <td>-10.096</td>\n",
- " <td>1</td>\n",
- " <td>2</td>\n",
- " <td>0.0380</td>\n",
- " <td>101.665</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
+ "image/png": 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",
"text/plain": [
- " valence acousticness danceability duration_ms explicit \\\n",
- "0 0.0594 0.982 0.279 831667 0 \n",
- "1 0.9630 0.732 0.819 180533 0 \n",
- "2 0.0394 0.961 0.328 500062 0 \n",
- "3 0.1650 0.967 0.275 210000 0 \n",
- "4 0.2530 0.957 0.418 166693 0 \n",
- "\n",
- " instrumentalness key liveness loudness mode popularity speechiness \\\n",
- "0 0.878000 10 0.665 -20.096 1 4 0.0366 \n",
- "1 0.000000 7 0.160 -12.441 1 5 0.4150 \n",
- "2 0.913000 3 0.101 -14.850 1 5 0.0339 \n",
- "3 0.000028 5 0.381 -9.316 1 3 0.0354 \n",
- "4 0.000002 3 0.229 -10.096 1 2 0.0380 \n",
- "\n",
- " tempo \n",
- "0 80.954 \n",
- "1 60.936 \n",
- "2 110.339 \n",
- "3 100.109 \n",
- "4 101.665 "
+ "<Figure size 640x480 with 2 Axes>"
]
},
- "execution_count": 21,
"metadata": {},
- "output_type": "execute_result"
+ "output_type": "display_data"
}
],
"source": [
- "raw_data.head()"
+ "# Make copies of the data to allow easy exploration\n",
+ "df_train_exp_copy = df_train.copy() \n",
+ "y_train_exp = df_train_exp_copy.pop('label').to_numpy()\n",
+ "x_train_exp = df_train_exp_copy.to_numpy()\n",
+ "\n",
+ "\n",
+ "# Take a single image, and remove the color dimension by reshaping\n",
+ "image = x_train_exp[0].reshape((28,28)) / 255.0\n",
+ "\n",
+ "plt.figure()\n",
+ "plt.imshow(image, cmap=plt.cm.binary)\n",
+ "plt.colorbar()\n",
+ "plt.grid(False)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Drucken Sie jeweils ein Bild aus jeder Kategorie, um zu sehen, wie sie aussehen und wie sie sich unterscheiden."
]
},
{
"cell_type": "code",
- "execution_count": 23,
- "metadata": {
- "scrolled": true
- },
+ "execution_count": 13,
+ "metadata": {},
"outputs": [
{
"data": {
- "image/png": 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",
+ "image/png": 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",
"text/plain": [
- "<Figure size 1296x1296 with 2 Axes>"
+ "<Figure size 1000x1000 with 10 Axes>"
]
},
"metadata": {},
@@ -1192,352 +1300,415 @@
}
],
"source": [
- "f,ax=plt.subplots(figsize = (18,18))\n",
- "sns.heatmap(raw_data.corr(),annot= True,linewidths=0.5,fmt = \".1f\",ax=ax)\n",
- "plt.xticks(rotation=90)\n",
- "plt.yticks(rotation=0)\n",
- "plt.title('Correlation Map')\n",
+ "plt.figure(figsize=(10,10))\n",
+ "i = 0\n",
+ "for index in range(len(x_train_exp)):\n",
+ " label = y_train_exp[index]\n",
+ " image = x_train_exp[index] / 255.0\n",
+ " if label == i:\n",
+ " image = image.reshape((28,28))\n",
+ " plt.subplot(5,5,i+1)\n",
+ " plt.xticks([])\n",
+ " plt.yticks([])\n",
+ " plt.grid(False)\n",
+ " plt.imshow(image, cmap=plt.cm.binary)\n",
+ " plt.title(class_names[label])\n",
+ " i += 1\n",
+ " if i == 10:\n",
+ " break\n",
"plt.show()"
]
},
{
- "cell_type": "code",
- "execution_count": 24,
+ "cell_type": "markdown",
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([[<AxesSubplot:title={'center':'valence'}>,\n",
- " <AxesSubplot:title={'center':'acousticness'}>,\n",
- " <AxesSubplot:title={'center':'danceability'}>,\n",
- " <AxesSubplot:title={'center':'duration_ms'}>],\n",
- " [<AxesSubplot:title={'center':'explicit'}>,\n",
- " <AxesSubplot:title={'center':'instrumentalness'}>,\n",
- " <AxesSubplot:title={'center':'key'}>,\n",
- " <AxesSubplot:title={'center':'liveness'}>],\n",
- " [<AxesSubplot:title={'center':'loudness'}>,\n",
- " <AxesSubplot:title={'center':'mode'}>,\n",
- " <AxesSubplot:title={'center':'popularity'}>,\n",
- " <AxesSubplot:title={'center':'speechiness'}>],\n",
- " [<AxesSubplot:title={'center':'tempo'}>, <AxesSubplot:>,\n",
- " <AxesSubplot:>, <AxesSubplot:>]], dtype=object)"
- ]
- },
- "execution_count": 24,
- "metadata": {},
- "output_type": "execute_result"
+ "source": [
+ "## 3. Datenvorbereitung"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "editable": true,
+ "include": true,
+ "paragraph": "Datenvorbereitung",
+ "slideshow": {
+ "slide_type": ""
},
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 1800x1800 with 16 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "tags": []
+ },
"source": [
- "#Detect outliers and handle them\n",
- "raw_data.hist(figsize=(25,25), bins=50)"
+ "Die Data Preparation Phase beginnt mit der Aufteilung der Daten in Trainings-, Validierungs- und Testsets sowie der Skalierung der Merkmale. Anschließend werden die Bildformate von 784 auf 28x28 umgewandelt (falls als CSV mit 784 Spalten geladen) und die Labels in ein kategorisches Format konvertiert. Die Überprüfung der Datenformate gewährleistet, dass alle Daten im korrekten Format vorliegen."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 3.1. Test- und Trainingsdaten"
]
},
{
"cell_type": "code",
- "execution_count": 25,
+ "execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
- "#raw_data.describe()"
+ "df_train_copy = df_train.copy() \n",
+ "y_train = df_train_copy.pop('label').to_numpy()\n",
+ "x_train = df_train_copy.to_numpy()\n",
+ "df_val_copy = df_val.copy() \n",
+ "y_val = df_val_copy.pop('label').to_numpy()\n",
+ "x_val = df_val_copy.to_numpy()\n",
+ "df_test_copy = df_test.copy() \n",
+ "y_test = df_test_copy.pop('label').to_numpy()\n",
+ "x_test = df_test_copy.to_numpy()"
]
},
{
- "cell_type": "code",
- "execution_count": 26,
- "metadata": {
- "scrolled": true
- },
- "outputs": [
- {
- "data": {
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- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>valence</th>\n",
- " <th>acousticness</th>\n",
- " <th>danceability</th>\n",
- " <th>duration_ms</th>\n",
- " <th>explicit</th>\n",
- " <th>instrumentalness</th>\n",
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- " <td>0.0366</td>\n",
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- " <td>7</td>\n",
- " <td>0.160</td>\n",
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- " <td>0.4150</td>\n",
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- " <th>2</th>\n",
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- " <td>0.961</td>\n",
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- " <th>3</th>\n",
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- " <td>0.967</td>\n",
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- " <td>0.418</td>\n",
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- " <td>0</td>\n",
- " <td>0.000002</td>\n",
- " <td>3</td>\n",
- " <td>0.229</td>\n",
- " <td>-10.096</td>\n",
- " <td>1</td>\n",
- " <td>2</td>\n",
- " <td>0.0380</td>\n",
- " <td>101.665</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " valence acousticness danceability duration_ms explicit \\\n",
- "0 0.0594 0.982 0.279 831667 0 \n",
- "1 0.9630 0.732 0.819 180533 0 \n",
- "2 0.0394 0.961 0.328 500062 0 \n",
- "3 0.1650 0.967 0.275 210000 0 \n",
- "4 0.2530 0.957 0.418 166693 0 \n",
- "\n",
- " instrumentalness key liveness loudness mode popularity speechiness \\\n",
- "0 0.878000 10 0.665 -20.096 1 4 0.0366 \n",
- "1 0.000000 7 0.160 -12.441 1 5 0.4150 \n",
- "2 0.913000 3 0.101 -14.850 1 5 0.0339 \n",
- "3 0.000028 5 0.381 -9.316 1 3 0.0354 \n",
- "4 0.000002 3 0.229 -10.096 1 2 0.0380 \n",
- "\n",
- " tempo \n",
- "0 80.954 \n",
- "1 60.936 \n",
- "2 110.339 \n",
- "3 100.109 \n",
- "4 101.665 "
- ]
- },
- "execution_count": 26,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "cell_type": "markdown",
+ "metadata": {},
"source": [
- "df_dummies = pd.get_dummies(raw_data, drop_first=True) # 0-1 encoding for categorical values\n",
- "df_dummies.head()"
+ "### 3.2. Merkmalsskalierung"
]
},
{
"cell_type": "code",
- "execution_count": 28,
+ "execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
- "from sklearn.linear_model import LinearRegression\n",
- "from sklearn.preprocessing import StandardScaler\n",
- "from sklearn import metrics"
+ "x_train = x_train / 255.0\n",
+ "x_val = x_val / 255.0\n",
+ "x_test = x_test / 255.0"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Wandeln Sie die Bildform von 784 auf 28x28 um (nur wenn sie als CSV mit 784 Spalten geladen wurden)."
]
},
{
"cell_type": "code",
- "execution_count": 29,
+ "execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
- "target = df_dummies['popularity']\n",
- "predictors = df_dummies.drop(['popularity'], axis=1)"
+ "IMG_ROWS = 28\n",
+ "IMG_COLS = 28\n",
+ "IMAGE_SHAPE = (IMG_ROWS, IMG_COLS, 1) \n",
+ "x_train = x_train.reshape(x_train.shape[0], *IMAGE_SHAPE)\n",
+ "x_val = x_val.reshape(x_val.shape[0], *IMAGE_SHAPE)\n",
+ "x_test = x_test.reshape(x_test.shape[0], *IMAGE_SHAPE)"
]
},
{
- "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 4.1 Test and Training sets"
+ "### 3.3. Labels konvertieren"
]
},
{
"cell_type": "code",
- "execution_count": 30,
+ "execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
- "from sklearn.model_selection import train_test_split\n",
- "X_train, X_test, y_train, y_test = train_test_split(predictors, target, test_size=0.2, random_state=365)"
+ "y_train = to_categorical(y_train, 10)\n",
+ "y_val = to_categorical(y_val, 10)\n",
+ "y_test = to_categorical(y_test, 10)"
]
},
{
- "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Vor Verwendung von Modellen Spalten standardisieren"
+ "Überprüfen Sie die Datenformate, um sicherzustellen, dass die Daten im richtigen Format vorliegen."
]
},
{
"cell_type": "code",
- "execution_count": 31,
+ "execution_count": 18,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(54000, 28, 28, 1)\n",
+ "(54000, 10)\n",
+ "(6000, 28, 28, 1)\n",
+ "(6000, 10)\n",
+ "(10000, 28, 28, 1)\n",
+ "(10000, 10)\n"
+ ]
+ }
+ ],
"source": [
- "scaler = StandardScaler()\n",
- "scaler.fit(X_train)\n",
+ "print(x_train.shape)\n",
+ "print(y_train.shape)\n",
"\n",
- "X_train = scaler.transform(X_train)\n",
- "X_test = scaler.transform(X_test)"
+ "print(x_val.shape)\n",
+ "print(y_val.shape)\n",
+ "\n",
+ "print(x_test.shape)\n",
+ "print(y_test.shape)\n"
]
},
{
- "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 4.2 Lineares-Regressionsmodell und Evaluation"
+ "# 4. Datenmodell"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "editable": true,
+ "include": true,
+ "paragraph": "Datenmodell",
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": [
+ "datenmodell"
+ ]
+ },
+ "source": [
+ "In der Modellierungsphase wird die Architektur des Modells definiert, die verschiedene Arten von Schichten umfasst. Zunächst wurde ein einfaches DNN mit dichten Schichten ausprobiert, jedoch konnten verbesserte Ergebnisse erzielt werden, indem auf eine CNN-Architektur umgestellt wurde. Als Ausgangsarchitektur wurde die Implementierung von LeNet-5 gewählt und angepasst. Die Hyperparameter wurden mithilfe eines Keras-Optimierers optimiert, der verschiedene Kombinationen ausprobierte, um die aktuellen Parameter auszuwählen. Das Modell umfasst Schichten wie Dense für die Berechnung des Skalarprodukts, Dropout zur Vermeidung von Überanpassung, Flatten zum Flachlegen von Matrizen, MaxPooling2D zur Reduzierung der Eingabedimensionen und Conv2D für Faltungsoperationen auf den Eingabedaten."
]
},
{
"cell_type": "code",
- "execution_count": 33,
+ "execution_count": 19,
"metadata": {},
"outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\Users\\ar\\anaconda3\\Lib\\site-packages\\keras\\src\\layers\\convolutional\\base_conv.py:107: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
+ " super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
+ ]
+ },
{
"data": {
+ "text/html": [
+ "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
+ "</pre>\n"
+ ],
"text/plain": [
- "LinearRegression()"
+ "\u001b[1mModel: \"sequential\"\u001b[0m\n"
]
},
- "execution_count": 33,
"metadata": {},
- "output_type": "execute_result"
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”┳â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”┳â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”┓\n",
+ "┃<span style=\"font-weight: bold\"> Layer (type) </span>┃<span style=\"font-weight: bold\"> Output Shape </span>┃<span style=\"font-weight: bold\"> Param # </span>┃\n",
+ "┡â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”╇â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”╇â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”┩\n",
+ "│ conv2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span> │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ conv2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">9,248</span> │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ max_pooling2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ flatten (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6272</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dropout (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6272</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">802,944</span> │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">1,290</span> │\n",
+ "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+ "</pre>\n"
+ ],
+ "text/plain": [
+ "â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”┳â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”┳â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”┓\n",
+ "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
+ "┡â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”╇â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”╇â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”┩\n",
+ "│ conv2d (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m320\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m9,248\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ max_pooling2d (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ flatten (\u001b[38;5;33mFlatten\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6272\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dropout (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6272\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dense (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m802,944\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dense_1 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m) │ \u001b[38;5;34m1,290\u001b[0m │\n",
+ "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">813,802</span> (3.10 MB)\n",
+ "</pre>\n"
+ ],
+ "text/plain": [
+ "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m813,802\u001b[0m (3.10 MB)\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">813,802</span> (3.10 MB)\n",
+ "</pre>\n"
+ ],
+ "text/plain": [
+ "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m813,802\u001b[0m (3.10 MB)\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
+ "</pre>\n"
+ ],
+ "text/plain": [
+ "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
}
],
"source": [
- "reg = LinearRegression()\n",
- "reg.fit(X_train, y_train)"
+ "model = Sequential()\n",
+ "model.add(Conv2D(filters=32,kernel_size=3, activation='relu', padding='same', input_shape=(28, 28,1)))\n",
+ "model.add(Conv2D(filters=32,kernel_size=3,padding='same', activation='relu'))\n",
+ "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
+ "model.add(Flatten())\n",
+ "model.add(Dropout(0.40))\n",
+ "model.add(Dense(units=128, activation='relu'))\n",
+ "model.add(Dense(units=10, activation='softmax'))\n",
+ "model.summary()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Modell kompilieren"
]
},
{
"cell_type": "code",
- "execution_count": 34,
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "model.compile(\n",
+ " optimizer=tf.keras.optimizers.Adam(), \n",
+ " loss= tf.keras.losses.categorical_crossentropy, \n",
+ " metrics=['accuracy']\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Trainieren und fitten des Modells"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "training performance\n",
- "0.44567392235354486\n",
- "test performance\n",
- "0.4477587073403365\n"
+ "Epoch 1/10\n",
+ "844/844 - 9s - 10ms/step - accuracy: 0.8485 - loss: 0.4227 - val_accuracy: 0.9016 - val_loss: 0.2939\n",
+ "Epoch 2/10\n",
+ "844/844 - 7s - 9ms/step - accuracy: 0.8973 - loss: 0.2795 - val_accuracy: 0.9141 - val_loss: 0.2329\n",
+ "Epoch 3/10\n",
+ "844/844 - 7s - 9ms/step - accuracy: 0.9124 - loss: 0.2361 - val_accuracy: 0.9312 - val_loss: 0.2121\n",
+ "Epoch 4/10\n",
+ "844/844 - 9s - 10ms/step - accuracy: 0.9236 - loss: 0.2059 - val_accuracy: 0.9156 - val_loss: 0.2440\n",
+ "Epoch 5/10\n",
+ "844/844 - 9s - 10ms/step - accuracy: 0.9330 - loss: 0.1798 - val_accuracy: 0.9234 - val_loss: 0.1941\n",
+ "Epoch 6/10\n",
+ "844/844 - 9s - 10ms/step - accuracy: 0.9403 - loss: 0.1591 - val_accuracy: 0.9312 - val_loss: 0.1952\n",
+ "Epoch 7/10\n",
+ "844/844 - 9s - 11ms/step - accuracy: 0.9474 - loss: 0.1394 - val_accuracy: 0.9234 - val_loss: 0.1963\n",
+ "Epoch 8/10\n",
+ "844/844 - 9s - 11ms/step - accuracy: 0.9535 - loss: 0.1258 - val_accuracy: 0.9266 - val_loss: 0.2069\n",
+ "Epoch 9/10\n",
+ "844/844 - 10s - 12ms/step - accuracy: 0.9601 - loss: 0.1071 - val_accuracy: 0.9312 - val_loss: 0.2307\n",
+ "Epoch 10/10\n",
+ "844/844 - 9s - 11ms/step - accuracy: 0.9645 - loss: 0.0956 - val_accuracy: 0.9458 - val_loss: 0.2156\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\Users\\ar\\anaconda3\\Lib\\contextlib.py:155: UserWarning: Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches. You may need to use the `.repeat()` function when building your dataset.\n",
+ " self.gen.throw(typ, value, traceback)\n"
]
}
],
"source": [
- "print('training performance')\n",
- "print(reg.score(X_train,y_train))\n",
- "print('test performance')\n",
- "print(reg.score(X_test,y_test))"
+ "# Determine the maximum number of epochs\n",
+ "NUM_EPOCHS = 10\n",
+ "BATCH_SIZE = 64\n",
+ "\n",
+ "# Fit the model, \n",
+ "# specify the training data\n",
+ "# the total number of epochs\n",
+ "# and the validation data we just created \n",
+ "history = model.fit(\n",
+ " x_train,\n",
+ " y_train,\n",
+ " batch_size=BATCH_SIZE,\n",
+ " epochs=NUM_EPOCHS,\n",
+ " validation_data=(x_val, y_val), \n",
+ " validation_steps=10,\n",
+ " verbose =2\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Wir wollen besonders wissen wie sich der Loss (Verlust) über die Zeit entwickelt"
]
},
{
"cell_type": "code",
- "execution_count": 35,
- "metadata": {
- "scrolled": true
- },
+ "execution_count": 22,
+ "metadata": {},
"outputs": [
{
"data": {
- "image/png": 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",
+ "image/png": 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",
"text/plain": [
- "<Figure size 1152x576 with 1 Axes>"
+ "<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
@@ -1545,9 +1716,8 @@
},
{
"data": {
- "image/png": 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",
"text/plain": [
- "<Figure size 432x432 with 3 Axes>"
+ "<Figure size 640x480 with 0 Axes>"
]
},
"metadata": {},
@@ -1555,19 +1725,200 @@
}
],
"source": [
- "y_pred = reg.predict(X_test)\n",
- "test = pd.DataFrame({'Predicted':y_pred,'Actual':y_test})\n",
- "fig= plt.figure(figsize=(16,8))\n",
- "test = test.reset_index()\n",
- "test = test.drop(['index'],axis=1)\n",
- "plt.plot(test[:50])\n",
- "plt.legend(['Actual','Predicted'])\n",
- "sns.jointplot(x='Actual',y='Predicted',data=test,kind='reg',);"
+ "acc = history.history[\"accuracy\"]\n",
+ "val_acc = history.history[\"val_accuracy\"]\n",
+ "loss = history.history[\"loss\"]\n",
+ "val_loss = history.history[\"val_loss\"]\n",
+ "\n",
+ "epochs = range(len(acc))\n",
+ "\n",
+ "plt.plot(epochs, acc, \"darkgreen\", label=\"Training accuracy\")\n",
+ "plt.plot(epochs, val_acc, \"darkblue\", label=\"Validation accuracy\")\n",
+ "plt.plot(epochs, loss, \"lightgreen\", label=\"Training loss\")\n",
+ "plt.plot(epochs, val_loss, \"lightblue\", label=\"Validation loss\")\n",
+ "plt.title(\"Training and validation accuracy\")\n",
+ "plt.xlabel(\"Epochs\")\n",
+ "plt.ylabel(\"Precent/100\")\n",
+ "plt.legend(loc=0)\n",
+ "plt.figure()\n",
+ "\n",
+ "\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Es ist zu erkennen, dass das Modell recht gut funktioniert, aber nach der zweiten Epoche beginnt es zu überanpassen. Um dies zu verhindern, könnten wir verschiedene Trainings-Validierungs-Splits ausprobieren, mehr Dropouts hinzufügen oder Teile des Modells umstrukturieren."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Testdaten evaluieren"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "test_loss, test_accuracy = model.evaluate(x_test, y_test, verbose=0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Test loss: 0.22. Test accuracy: 93.17%\n"
+ ]
+ }
+ ],
+ "source": [
+ "print('Test loss: {0:.2f}. Test accuracy: {1:.2f}%'.format(test_loss, test_accuracy*100.))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Das Modell schneidet mit einer Genauigkeit von > 90 % bei den Testdaten recht gut ab. Der Verlust ist akzeptabel."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Zeige Resultate nach Klasse an"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[1m313/313\u001b[0m \u001b[32mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step\n"
+ ]
+ }
+ ],
+ "source": [
+ "predicted_classes = (model.predict(x_test) > 0.5).astype(\"int32\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " precision recall f1-score support\n",
+ "\n",
+ " Top 0.91 0.85 0.88 1000\n",
+ " Trouser 0.99 0.99 0.99 1000\n",
+ " Pullover 0.89 0.89 0.89 1000\n",
+ " Dress 0.92 0.97 0.94 1000\n",
+ " Coat 0.93 0.86 0.89 1000\n",
+ " Sandal 0.99 0.98 0.99 1000\n",
+ " Shirt 0.81 0.82 0.81 1000\n",
+ " Sneaker 0.96 0.98 0.97 1000\n",
+ " Bag 0.98 0.99 0.99 1000\n",
+ " Ankle boot 0.98 0.96 0.97 1000\n",
+ "\n",
+ " micro avg 0.94 0.93 0.93 10000\n",
+ " macro avg 0.94 0.93 0.93 10000\n",
+ "weighted avg 0.94 0.93 0.93 10000\n",
+ " samples avg 0.93 0.93 0.93 10000\n",
+ "\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\Users\\ar\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1517: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in samples with no predicted labels. Use `zero_division` parameter to control this behavior.\n",
+ " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(classification_report(y_test, predicted_classes, target_names=class_names))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 5. Evaluation"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "editable": true,
+ "include": true,
+ "paragraph": "Evaluation",
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": [
+ "evaluation"
+ ]
+ },
+ "source": [
+ "Nach dem Training und der Validierung des Modells wird die Leistung anhand der Testdaten ausgewertet. Dabei erreicht das Modell eine Testgenauigkeit von über 90 %, was darauf hinweist, dass es gut in der Lage ist, neue, bisher ungesehene Daten zu klassifizieren. Der Testverlust ist ebenfalls akzeptabel niedrig, was darauf hinweist, dass das Modell Vorhersagen nahe an den tatsächlichen Daten trifft. Zusätzlich zeigen klassenspezifische Metriken wie Präzision, Rückruf und F1-Score für jede Klasse (z.B. Top, Trouser, Pullover usw.), dass das Modell gute bis sehr gute Ergebnisse erzielt, insbesondere bei Klassen wie Sneaker, Bag und Ankle boot. Diese Ergebnisse bieten eine umfassende Bewertung der Modellleistung und helfen dabei, seine Eignung für praktische Anwendungen zu beurteilen."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "editable": true,
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
+ "source": [
+ "## 6. Umsetzung"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "editable": true,
+ "include": true,
+ "paragraph": "Umsetzung",
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": [
+ "umsetzung"
+ ]
+ },
+ "source": [
+ "Im Rahmen des CRISP-DM Zyklus stellt das Deployment den letzten Schritt dar, bei dem das trainierte Modell für den produktiven Einsatz vorbereitet wird. Dies beinhaltet die Implementierung des Modells in eine Produktionsumgebung, wo es Echtzeitdaten verarbeiten kann. Vor dem Deployment müssen alle Aspekte wie Modellperformance auf Testdaten, Sicherstellung der Skalierbarkeit und Integration in bestehende Systeme sorgfältig überprüft werden. Zudem ist es entscheidend, fortlaufende Überwachung und Wartung sicherzustellen, um die langfristige Leistung und Genauigkeit des Modells zu gewährleisten."
]
}
],
"metadata": {
+ "branche": "Medien",
"category": "Marketing",
+ "dataSource": "https://storage.googleapis.com/ml-service-repository-datastorage/Generation_of_Individual_Playlists_Generation-of-Individual-Playlists-data.csv",
+ "funktion": "Operations",
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
@@ -1585,7 +1936,10 @@
"pygments_lexer": "ipython3",
"version": "3.11.7"
},
- "title": "Generation of Individual Playlists"
+ "repoLink": "https://github.com/AlexRossmann/machine-learning-services/tree/main/Marketing/Generation%20of%20Individual%20Playlists",
+ "skipNotebookInDeployment": false,
+ "teaser": "In diesem Praxisbeispiel können Sie mithilfe des Machine Learning Modells personalisierte Spotify Playlists erstellen, die dem Musikgeschmack des Endnutzers entsprechen. ",
+ "title": "Generierung individueller Playlists"
},
"nbformat": 4,
"nbformat_minor": 4
diff --git a/Warehouse/.DS_Store b/Warehouse/.DS_Store
deleted file mode 100644
index 966cd29ba669886507f49fd5fe5c756338a41a4f..0000000000000000000000000000000000000000
Binary files a/Warehouse/.DS_Store and /dev/null differ
diff --git a/Warehouse/Classification of clothing through images/.DS_Store b/Warehouse/Classification of clothing through images/.DS_Store
deleted file mode 100644
index a929ae54618bc386a49c88351dac4a3b2c5e4ced..0000000000000000000000000000000000000000
Binary files a/Warehouse/Classification of clothing through images/.DS_Store and /dev/null differ
diff --git a/Warehouse/Classification of clothing through images/notebook.ipynb b/Warehouse/Classification of clothing through images/notebook.ipynb
index 87a5c50d6e2c5f1849420883a346711e5b2d43af..adeb1661ac11cd907375a4ae3bffa9b24582fd92 100644
--- a/Warehouse/Classification of clothing through images/notebook.ipynb
+++ b/Warehouse/Classification of clothing through images/notebook.ipynb
@@ -67,129 +67,11 @@
"\n",
"Der Datensatz umfasst 784 Merkmale, die jeweils einem Pixel des Bildes entsprechen, sowie ein zusätzliches Label, das die Kategorie des Kleidungsstücks angibt. Sowohl die Merkmale als auch die Labels sind als Integer-Werte gespeichert. Die Pixelwerte repräsentieren die Intensität des Grautons, während die Labels die verschiedenen Kleidungsstückkategorien darstellen.\n",
"\n",
- "Insgeamt enthält der Datensatz 70.000 Beobachtungen. Die Parameter \"Standort\", Verteilungsparameter und Korrelationsanalyse sind für diesen Datensatz nicht anwendbar.\n",
+ "Insgesamt enthält der Datensatz 70.000 Beobachtungen. Die Parameter \"Standort\", Verteilungsparameter und Korrelationsanalyse sind für diesen Datensatz nicht anwendbar.\n",
"\n",
"Der Fashion-MNIST-Datensatz bietet eine moderne und herausfordernde Alternative zum klassischen MNIST-Datensatz, da er realistischere und komplexere Bilder von Kleidungsstücken enthält. Dies stellt eine größere Herausforderung für Bildklassifizierungsmodelle dar und bietet eine realistischere Anwendungsmöglichkeit im Bereich der maschinellen Bildverarbeitung."
]
},
- {
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "source": [
- "## 3. Datenvorbereitung"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph": "Datenvorbereitung",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "source": [
- "Die Data Preparation Phase beginnt mit der Aufteilung der Daten in Trainings-, Validierungs- und Testsets sowie der Skalierung der Merkmale. Anschließend werden die Bildformate von 784 auf 28x28 umgewandelt (falls als CSV mit 784 Spalten geladen) und die Labels in ein kategorisches Format konvertiert. Die Überprüfung der Datenformate gewährleistet, dass alle Daten im korrekten Format vorliegen."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "source": [
- "# 4. Datenmodell"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph": "Datenmodell",
- "slideshow": {
- "slide_type": ""
- },
- "tags": [
- "datenmodell"
- ]
- },
- "source": [
- "In der Modellierungsphase wird die Architektur des Modells definiert, die verschiedene Arten von Schichten umfasst. Zunächst wurde ein einfaches DNN mit dichten Schichten ausprobiert, jedoch konnten verbesserte Ergebnisse erzielt werden, indem auf eine CNN-Architektur umgestellt wurde. Als Ausgangsarchitektur wurde die Implementierung von LeNet-5 gewählt und angepasst. Die Hyperparameter wurden mithilfe eines Keras-Optimierers optimiert, der verschiedene Kombinationen ausprobierte, um die aktuellen Parameter auszuwählen. Das Modell umfasst Schichten wie Dense für die Berechnung des Skalarprodukts, Dropout zur Vermeidung von Überanpassung, Flatten zum Flachlegen von Matrizen, MaxPooling2D zur Reduzierung der Eingabedimensionen und Conv2D für Faltungsoperationen auf den Eingabedaten."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "source": [
- "## 5. Evaluation"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph": "Evaluation",
- "slideshow": {
- "slide_type": ""
- },
- "tags": [
- "evaluation"
- ]
- },
- "source": [
- "Nach dem Training und der Validierung des Modells wird die Leistung anhand der Testdaten ausgewertet. Dabei erreicht das Modell eine Testgenauigkeit von über 90 %, was darauf hinweist, dass es gut in der Lage ist, neue, bisher ungesehene Daten zu klassifizieren. Der Testverlust ist ebenfalls akzeptabel niedrig, was darauf hinweist, dass das Modell Vorhersagen nahe an den tatsächlichen Daten trifft. Zusätzlich zeigen klassenspezifische Metriken wie Präzision, Rückruf und F1-Score für jede Klasse (z.B. Top, Trouser, Pullover usw.), dass das Modell gute bis sehr gute Ergebnisse erzielt, insbesondere bei Klassen wie Sneaker, Bag und Ankle boot. Diese Ergebnisse bieten eine umfassende Bewertung der Modellleistung und helfen dabei, seine Eignung für praktische Anwendungen zu beurteilen."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "source": [
- "## 6. Umsetzung"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph": "Umsetzung",
- "slideshow": {
- "slide_type": ""
- },
- "tags": [
- "umsetzung"
- ]
- },
- "source": [
- "Im Rahmen des CRISP-DM Zyklus stellt das Deployment den letzten Schritt dar, bei dem das trainierte Modell für den produktiven Einsatz vorbereitet wird. Dies beinhaltet die Implementierung des Modells in eine Produktionsumgebung, wo es Echtzeitdaten verarbeiten kann. Vor dem Deployment müssen alle Aspekte wie Modellperformance auf Testdaten, Sicherstellung der Skalierbarkeit und Integration in bestehende Systeme sorgfältig überprüft werden. Zudem ist es entscheidend, fortlaufende Überwachung und Wartung sicherzustellen, um die langfristige Leistung und Genauigkeit des Modells zu gewährleisten."
- ]
- },
{
"cell_type": "markdown",
"metadata": {},
@@ -1441,6 +1323,34 @@
"plt.show()"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "editable": true,
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
+ "source": [
+ "## 3. Datenvorbereitung"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "editable": true,
+ "include": true,
+ "paragraph": "Datenvorbereitung",
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
+ "source": [
+ "Die Data Preparation Phase beginnt mit der Aufteilung der Daten in Trainings-, Validierungs- und Testsets sowie der Skalierung der Merkmale. Anschließend werden die Bildformate von 784 auf 28x28 umgewandelt (falls als CSV mit 784 Spalten geladen) und die Labels in ein kategorisches Format konvertiert. Die Überprüfung der Datenformate gewährleistet, dass alle Daten im korrekten Format vorliegen."
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
@@ -1558,6 +1468,36 @@
"print(y_test.shape)\n"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "editable": true,
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
+ "source": [
+ "# 4. Datenmodell"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "editable": true,
+ "include": true,
+ "paragraph": "Datenmodell",
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": [
+ "datenmodell"
+ ]
+ },
+ "source": [
+ "In der Modellierungsphase wird die Architektur des Modells definiert, die verschiedene Arten von Schichten umfasst. Zunächst wurde ein einfaches DNN mit dichten Schichten ausprobiert, jedoch konnten verbesserte Ergebnisse erzielt werden, indem auf eine CNN-Architektur umgestellt wurde. Als Ausgangsarchitektur wurde die Implementierung von LeNet-5 gewählt und angepasst. Die Hyperparameter wurden mithilfe eines Keras-Optimierers optimiert, der verschiedene Kombinationen ausprobierte, um die aktuellen Parameter auszuwählen. Das Modell umfasst Schichten wie Dense für die Berechnung des Skalarprodukts, Dropout zur Vermeidung von Überanpassung, Flatten zum Flachlegen von Matrizen, MaxPooling2D zur Reduzierung der Eingabedimensionen und Conv2D für Faltungsoperationen auf den Eingabedaten."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 19,
@@ -1934,6 +1874,66 @@
"source": [
"print(classification_report(y_test, predicted_classes, target_names=class_names))"
]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "editable": true,
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
+ "source": [
+ "## 5. Evaluation"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "editable": true,
+ "include": true,
+ "paragraph": "Evaluation",
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": [
+ "evaluation"
+ ]
+ },
+ "source": [
+ "Nach dem Training und der Validierung des Modells wird die Leistung anhand der Testdaten ausgewertet. Dabei erreicht das Modell eine Testgenauigkeit von über 90 %, was darauf hinweist, dass es gut in der Lage ist, neue, bisher ungesehene Daten zu klassifizieren. Der Testverlust ist ebenfalls akzeptabel niedrig, was darauf hinweist, dass das Modell Vorhersagen nahe an den tatsächlichen Daten trifft. Zusätzlich zeigen klassenspezifische Metriken wie Präzision, Rückruf und F1-Score für jede Klasse (z.B. Top, Trouser, Pullover usw.), dass das Modell gute bis sehr gute Ergebnisse erzielt, insbesondere bei Klassen wie Sneaker, Bag und Ankle boot. Diese Ergebnisse bieten eine umfassende Bewertung der Modellleistung und helfen dabei, seine Eignung für praktische Anwendungen zu beurteilen."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "editable": true,
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
+ "source": [
+ "## 6. Umsetzung"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "editable": true,
+ "include": true,
+ "paragraph": "Umsetzung",
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": [
+ "umsetzung"
+ ]
+ },
+ "source": [
+ "Im Rahmen des CRISP-DM Zyklus stellt das Deployment den letzten Schritt dar, bei dem das trainierte Modell für den produktiven Einsatz vorbereitet wird. Dies beinhaltet die Implementierung des Modells in eine Produktionsumgebung, wo es Echtzeitdaten verarbeiten kann. Vor dem Deployment müssen alle Aspekte wie Modellperformance auf Testdaten, Sicherstellung der Skalierbarkeit und Integration in bestehende Systeme sorgfältig überprüft werden. Zudem ist es entscheidend, fortlaufende Überwachung und Wartung sicherzustellen, um die langfristige Leistung und Genauigkeit des Modells zu gewährleisten."
+ ]
}
],
"metadata": {