def main():
np.random.seed(311)
# import the datasets
x_census_data, y_census_data = process_data.process_census_data(
'./../Datasets/Census_Income')
x_stock_data, y_stock_data = process_data.process_stock_data(
'./../Datasets/Stocks', ["2016", "2017"])
stock_params = {
'variance': range(1, 221),
'name': "Stock Data",
'svd_graph_recon': "Stock Data Reconstruction Graph of SVD",
'n_components': 100,
'filename': "stock_data_svd.txt",
'svd_graph': "Stock Data - Data Plotted on Principal Axes using SVD"
}
census_params = {
'variance': range(1, 13),
'name': "Census Data",
'svd_graph_recon': "Census Data Reconstruction Graph of SVD",
'n_components': 4,
'filename': "census_data_svd.txt",
'svd_graph': "Census Data - Data Plotted on Principal Axes using SVD"
}
run_svd_reconstruction(stock_params, x_stock_data)
run_svd_reconstruction(census_params, x_census_data)
run_svd(stock_params, x_stock_data, y_stock_data)
run_svd(census_params, x_census_data, y_census_data)
def main():
x_gas_data, y_gas_data = process_data.process_gas_data('./../Datasets/Gas_Data')
x_stock_data, y_stock_data = process_data.process_stock_data('./../Datasets/Stocks', ["2016"])
x_census_data, y_census_data = process_data.process_census_data('./../Datasets/Census_Income')
# run_decision_tree(x_gas_data, y_gas_data, "Gas Sensor Data")
# run_decision_tree(x_stock_data, y_stock_data, "Stock Data")
run_decision_tree(x_census_data, y_census_data, "Census Data")
def main():
np.random.seed(311)
# import the datasets
x_census_data, y_census_data = process_data.process_census_data(
'./../Datasets/Census_Income')
x_stock_data, y_stock_data = process_data.process_stock_data(
'./../Datasets/Stocks', ["2016", "2017"])
stock_params = {
'variance': range(1, 221),
'name': "Stock Data",
'ica_graph_recon': "Stock Data Reconstruction Graph of ICA",
'n_components': 100,
'filename': "stock_data_ica.txt",
'ica_title': 'Stock Data Kurtosis of Calculated Components',
'ica_graph': "Stock Data on Principle Components of ICA"
}
census_params = {
'variance': range(1, 14),
'name': "Census Data",
'ica_graph_recon': "Census Data Reconstruction Graph of ICA",
'n_components': 4,
'filename': "census_data_ica.txt",
'ica_title': 'Census Data Kurtosis of Calculated Components',
'ica_graph': "Census Data on Principle Components of ICA"
}
run_ica_reconstruction(stock_params, x_stock_data)
run_ica_reconstruction(census_params, x_census_data)
run_ica(stock_params, x_stock_data, y_stock_data)
run_ica(census_params, x_census_data, y_census_data)
def main():
x_gas_data, y_gas_data = process_data.process_gas_data(
'./../Datasets/Gas_Data')
x_stock_data, y_stock_data = process_data.process_stock_data(
'./../Datasets/Stocks', ["2016"])
run_knn_learning(x_gas_data, y_gas_data, "Gas Sensor Data")
run_knn_learning(x_stock_data, y_stock_data, "Stock Data")
def main():
x_gas_data, y_gas_data = process_data.process_gas_data(
'./../Datasets/Gas_Data')
x_bank_data, y_bank_data = process_data.process_bank_data(
'./../Datasets/Banking')
x_letter_data, y_letter_data = process_data.process_letter_data(
'./../Datasets/Letter')
x_spam_data, y_spam_data = process_data.process_spam_data(
'./../Datasets/Spam')
x_stock_data, y_stock_data = process_data.process_stock_data(
'./../Datasets/Stocks', ["2016"])
neural_network_learning(x_gas_data, y_gas_data, "Gas Sensor Data")
# neural_network_learning(x_bank_data, y_bank_data, "Bank Data")
# neural_network_learning(x_letter_data, y_letter_data, "Letter Data")
# neural_network_learning(x_spam_data, y_spam_data, "Spam Data")
neural_network_learning(x_stock_data, y_stock_data, "Stock Data")
def main():
x_gas_data, y_gas_data = process_data.process_gas_data(
'./../Datasets/Gas_Data')
x_bank_data, y_bank_data = process_data.process_bank_data(
'./../Datasets/Banking')
x_letter_data, y_letter_data = process_data.process_letter_data(
'./../Datasets/Letter')
x_spam_data, y_spam_data = process_data.process_spam_data(
'./../Datasets/Spam')
x_stock_data, y_stock_data = process_data.process_stock_data(
'./../Datasets/Stocks', ["2016"])
run_boosted_learning(x_gas_data, y_gas_data, "Gas Sensor Data")
# run_decision_tree(x_bank_data, y_bank_data, "Bank Data")
# run_decision_tree(x_letter_data, y_letter_data, "Letter Data")
# run_boosted_learning(x_spam_data, y_spam_data, "Spam Data")
# run_boosted_learning_deep(x_gas_data, y_gas_data, "Gas Sensor Data")
run_boosted_learning(x_stock_data, y_stock_data, "Stock Data")
def main():
timer = time.time()
x_gas_data, y_gas_data = process_data.process_gas_data(
'./../Datasets/Gas_Data')
x_census_data, y_census_data = process_data.process_census_data(
'./../Datasets/Census_Income')
x_stock_data, y_stock_data = process_data.process_stock_data(
'./../Datasets/Stocks', ["2016", "2017"])
print("\n---------- Running Data on Decision Tree Learner -------------\n")
decision_tree_learning.run_decision_tree(x_census_data, y_census_data,
"Census Data")
decision_tree_learning.run_decision_tree(x_stock_data, y_stock_data,
"Stock Data")
print(
"\n---------- Running Data on Artificial Neural Network Learner -------------\n"
)
ann_learning.neural_network_learning(x_census_data, y_census_data,
"Census Data")
ann_learning.neural_network_learning(x_stock_data, y_stock_data,
"Stock Data")
print(
"\n---------- Running Data on Decision Tree Learner with Adaboost -------------\n"
)
boosted_learning.run_boosted_learning(x_census_data, y_census_data,
"Census Data")
boosted_learning.run_boosted_learning(x_stock_data, y_stock_data,
"Stock Data")
print(
"\n---------- Running Data on Support Vector Learner -------------\n")
support_vector_learning.run_svc_learning(x_census_data, y_census_data,
"Census Data")
support_vector_learning.run_svc_learning(x_stock_data, y_stock_data,
"Stock Data")
print("\n---------- Running Data on KNN Learner -------------\n")
knn_learning.run_knn_learning(x_census_data, y_census_data, "Census Data")
knn_learning.run_knn_learning(x_stock_data, y_stock_data, "Stock Data")
print("Total Execution time: " + str(time.time() - timer) + "s")