def _common(self, Z, y):
scale = Scaler(Z)
transform = compose(prepend_x0, Scaler.normalize)
X = transform(scale)
data = zip(X, y)
h_theta0 = [0.] * len(X[0])
coeff = compose(scale.denormalize, get(0),
lin_reg(J, gradJ, h_theta0, it_max=2000))
h_thetad = coeff(data)
return h_thetad
Z, q = csv_reader('./data/iris.csv', ['SL', 'SW', 'PL', 'PW'], 'Type')
# Get Sepal Length and Petal Length features
Zp = list(pluck([0, 2], Z))
# Get only the Iris Setosa (0) and Iris Versicolour (1) classes
datap = [[f, o] for f, o in zip(Zp, q) if o != 2.0]
Xp, yp = zip(*datap)
y = list(yp)
Xpp = [list(e) for e in Xp]
print(Xpp)
print(y)
# Split set into training and testing data
train_data, test_data = train_test_split(zip(Xpp, y), 0.33)
# Scale the data
X_train, y_train = zip(*train_data)
scale = Scaler()
scale.fit(X_train)
transform = compose(prepend_x0, scale.transform)
scaledX_train = transform(X_train)
scaled_train = zip(scaledX_train, y_train)
# Fit the training data
h_theta0 = [1., 1., 1.]
print('****Gradient Descent****\n')
print('--Training--\n')
h_thetaf, cost = glm.fit(logr.logistic_log_likelihood,
logr.grad_logistic,
h_theta0,
scaled_train,
eta=0.03,
it_max=500,
gf='gd')
