def test_dim():
# Get Data
data_getter = TestDataGetter(10, 9)
X = data_getter.get_x_data()
y = data_getter.get_y_data()
# Shuffle the data and split it
spliter = DataSpliter(X, y, 0.5, 0, 0.5)
X_train, y_train = spliter.get_training_set()
X_test, y_test = spliter.get_testing_set()
# Dimensionality Reduction
print("LDA")
for i in range(1,3):
dimred = DimensionReduction(X_train, y_train, X_test, i)
X_reduced_train, X_reduced_test = dimred.lda_data()
test_GNB(X_reduced_train, y_train, X_reduced_test, y_test, i)
print("PCA")
for i in range(1,30):
dimred = DimensionReduction(X_train, y_train, X_test, i)
X_reduced_train, X_reduced_test = dimred.pca_data()
test_GNB(X_reduced_train, y_train, X_reduced_test, y_test, i)
print("FA")
for i in range(1,30):
dimred = DimensionReduction(X_train, y_train, X_test, i)
X_reduced_train, X_reduced_test = dimred.fa_2D_data()
test_GNB(X_reduced_train, y_train, X_reduced_test, y_test, i)
def main():
# Get Data
data_getter = TestDataGetter(5, 4)
X = data_getter.get_x_data()
y = data_getter.get_y_data()
# Dimensionality Reduction
dimred = DimensionReduction(X, y, X)
X_lda_2d = dimred.lda_2D_data()
# GPC Plotter
plot_GPC(X_lda_2d, y, 5)
def main():
# Get Data
data_getter = TestDataGetter(5, 4)
X = data_getter.get_x_data()
y = data_getter.get_y_data()
# Shuffle the data and split it
spliter = DataSpliter(X, y, 0.6, 0, 0.4)
X_train, y_train = spliter.get_training_set()
X_test, y_test = spliter.get_testing_set()
# Dimensionality Reduction
dimred = DimensionReduction(X_train, y_train, X_test)
X_reduced_train, X_reduced_test = dimred.lda_data()
test_GNB(X_reduced_train, y_train, X_reduced_test, y_test)
def main():
# Get Data
data_getter = TestDataGetter(10, 9)
X = data_getter.get_x_data(used_for="switch")
y = data_getter.get_y_data(used_for="switch")
# Data Spliter
spliter = DataSpliter(X, y, 0.7, 0, 0.3)
X_train, y_train = spliter.get_training_set()
X_test, y_test = spliter.get_testing_set()
# Dimensionality Reduction
dimred = DimensionReduction(X_train, y_train, X_test)
X_reduced_train, X_reduced_test = dimred.lda_data()
# Gaussian Naive Bayes Classifier
switch_detector(X_reduced_train, y_train, X_reduced_test, y_test)
def get_GNB_test_points():
data_getter = TestDataGetter(5, 4)
X = data_getter.get_x_data()
y = data_getter.get_y_data()
# Dimensionality Reduction
dimred = DimensionReduction(X, y)
# X_lda_2d = get_LDA()
gnb = GaussianNB().fit(X_lda_2d, y)
return X, gnb.predict(X_lda_2d)
def main():
# Get Data
data_getter = TestDataGetter(5, 4)
X = data_getter.get_x_data()
y = data_getter.get_y_data()
# Shuffle the data and split it
spliter = DataSpliter(X, y, 0.6, 0, 0.4)
X_train, y_train = spliter.get_training_set()
X_test, y_test = spliter.get_testing_set()
# Dimensionality Reduction
dimred = DimensionReduction(X_train, y_train, X_test)
X_reduced_trian, X_reduced_test = dimred.lda_data()
# K-NN Tester
# X_test, y_test = spliter.get_testing_set()
# test_err = test_kNN(X_reduced_trian, y_train, X_reduced_test, y_test, 13)
# print("Final Test Error: ", test_err)
# K-NN Ploter
plot_kNN(X_reduced_trian, y_train)