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 test_explained_variance_LDA():
# 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, 1, 0, 0)
X_train, y_train = spliter.get_training_set()
# Dimensionality Reduction
for i in range(1, 31):
reductor = LinearDiscriminantAnalysis(n_components=i).fit(X_train, y_train)
print("Dim = %2d: explained variance ratio:" % i, sum(reductor.explained_variance_ratio_))
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, 1, 0, 0)
X_train, y_train = spliter.get_training_set()
# Dimensionality Reduction
dimred = DimensionReduction(X_train, y_train, X_train)
# Draw LDA Graphs
X_lda_2d, _ = dimred.lda_data()
plot_gst_clf_scatter_2D(X_lda_2d, y_train, '32-LDA of Three Basic Gestures')
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 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)