def run(self):
# using gradDescent to get the optimal parameter beta = [w, b] in page-59
beta = self_def.gradDscent_2(self.X_train, self.y_train)
# prediction, beta mapping to the model
y_pred = self_def.predict(self.X_test, beta)
m_test = np.shape(self.X_test)[0]
# calculation of confusion_matrix and prediction accuracy
cfmat = np.zeros((2, 2))
for i in range(m_test):
if y_pred[i] == self.y_test[i] == 0:
cfmat[0, 0] += 1
elif y_pred[i] == self.y_test[i] == 1:
cfmat[1, 1] += 1
elif y_pred[i] == 0:
cfmat[1, 0] += 1
elif y_pred[i] == 1:
cfmat[0, 1] += 1
print(cfmat)
pass
from sklearn import model_selection
import self_def
# X_train, X_test, y_train, y_test
np.ones(n)
m, n = np.shape(X)
X_ex = np.c_[X, np.ones(m)] # extend the variable matrix to [x, 1]
#print (X_ex)
X_train, X_test, y_train, y_test = model_selection.train_test_split(X_ex, y, test_size=0.5, random_state=0)
# using gradDescent to get the optimal parameter beta = [w, b] in page-59
beta = self_def.gradDscent_1(X_train, y_train)
# prediction, beta mapping to the model
y_pred = self_def.predict(X_test, beta)
m_test = np.shape(X_test)[0]
# calculation of confusion_matrix and prediction accuracy
cfmat = np.zeros((2, 2))
for i in range(m_test):
if y_pred[i] == y_test[i] == 0:
cfmat[0, 0] += 1
elif y_pred[i] == y_test[i] == 1:
cfmat[1, 1] += 1
elif y_pred[i] == 0:
cfmat[1, 0] += 1
elif y_pred[i] == 1:
cfmat[0, 1] += 1
print(cfmat)
from sklearn import model_selection
import self_def
# X_train, X_test, y_train, y_test
np.ones(n)
m,n = np.shape(X)
X_ex = np.c_[X, np.ones(m)] # extend the variable matrix to [x, 1]
X_train, X_test, y_train, y_test = model_selection.train_test_split(X_ex, y, test_size=0.5, random_state=0)
# using gradDescent to get the optimal parameter beta = [w, b] in page-59
beta = self_def.gradDscent_2(X_train, y_train)
# prediction, beta mapping to the model
y_pred = self_def.predict(X_test, beta)
m_test = np.shape(X_test)[0]
# calculation of confusion_matrix and prediction accuracy
cfmat = np.zeros((2,2))
for i in range(m_test):
if y_pred[i] == y_test[i] == 0: cfmat[0,0] += 1
elif y_pred[i] == y_test[i] == 1: cfmat[1,1] += 1
elif y_pred[i] == 0: cfmat[1,0] += 1
elif y_pred[i] == 1: cfmat[0,1] += 1
print(cfmat)