def run(self, X, y, alpha=0.01, num_iterations=10000, step_size=0.000001):
'''
INPUT: GradientAscent, 2 dimensional numpy array, numpy array
float, int
OUTPUT: None
Run the gradient ascent algorithm for num_iterations repititions. Use
the gradient method and the learning rate alpha to update the
coefficients at each iteration.
'''
if self.scale:
X = self.scale_X(X)
if self.fit_intercept:
X = add_intercept(X)
self.coeffs = np.zeros(X.shape[1])
if step_size:
cost1 = self.cost(X, y, self.coeffs)
while True:
self.coeffs = self.coeffs + alpha * self.gradient(
X, y, self.coeffs, l=1)
cost2 = self.cost(X, y, self.coeffs)
if abs(cost2 - cost1) < step_size:
print cost2
break
else:
cost1 = cost2
else:
for i in xrange(num_iterations):
self.coeffs = self.coeffs + alpha * self.gradient(
X, y, self.coeffs, l=1)
def s_run(self, X, y, alpha=0.01, num_iterations=100000, step_size=0.0000000001):
if self.scale:
X = self.scale_X(X)
if self.fit_intercept:
X = add_intercept(X)
self.coeffs = np.zeros(X.shape[1])
if step_size:
cost1 = self.cost(X, y, self.coeffs)
while True:
a = range(len(X))
np.random.shuffle(a)
for i in a:
self.coeffs = self.coeffs + alpha * self.gradient(X[i], y[i], self.coeffs, l=1)
# if abs(cost2 - cost1) < step_size:
# break
# else:
# cost1 = cost2
cost2 = self.cost(X, y, self.coeffs)
if abs(cost2 - cost1) < step_size:
break
else:
cost1 = cost2
else:
print "step_size parameter must be initialized!"
for i in xrange(num_iterations):
np.random.shuffle(X)
for i, x in enumerate(X):
self.coeffs = self.coeffs + alpha * self.gradient(x, y[i], self.coeffs, l=1)
def run(self, X, y, alpha=0.01, num_iterations=10000, step_size=0.000001):
'''
INPUT: GradientAscent, 2 dimensional numpy array, numpy array
float, int
OUTPUT: None
Run the gradient ascent algorithm for num_iterations repititions. Use
the gradient method and the learning rate alpha to update the
coefficients at each iteration.
'''
if self.scale:
X = self.scale_X(X)
if self.fit_intercept:
X = add_intercept(X)
self.coeffs = np.zeros(X.shape[1])
if step_size:
cost1 = self.cost(X, y, self.coeffs)
while True:
self.coeffs = self.coeffs + alpha * self.gradient(X, y, self.coeffs, l=1)
cost2 = self.cost(X, y, self.coeffs)
if abs(cost2 - cost1) < step_size:
print cost2
break
else:
cost1 = cost2
else:
for i in xrange(num_iterations):
self.coeffs = self.coeffs + alpha * self.gradient(X, y, self.coeffs, l=1)
def predict(self, X):
'''
INPUT: GradientAscent, 2 dimensional numpy array
OUTPUT: numpy array (ints)
Use the coeffs to compute the prediction for X. Return an array of 0's
and 1's. Call self.predict_func.
'''
if self.scale:
X = self.scale_X(X)
if self.fit_intercept:
X = add_intercept(X)
return self.predict_func(X, self.coeffs)
def predict(self, X):
'''
INPUT: GradientAscent, 2 dimensional numpy array
OUTPUT: numpy array (ints)
Use the coeffs to compute the prediction for X. Return an array of 0's
and 1's. Call self.predict_func.
'''
if self.scale:
X = self.scale_X(X)
if self.fit_intercept:
X = add_intercept(X)
return self.predict_func(X, self.coeffs)
def s_run(self,
X,
y,
alpha=0.01,
num_iterations=100000,
step_size=0.0000000001):
if self.scale:
X = self.scale_X(X)
if self.fit_intercept:
X = add_intercept(X)
self.coeffs = np.zeros(X.shape[1])
if step_size:
cost1 = self.cost(X, y, self.coeffs)
while True:
a = range(len(X))
np.random.shuffle(a)
for i in a:
self.coeffs = self.coeffs + alpha * self.gradient(
X[i], y[i], self.coeffs, l=1)
# if abs(cost2 - cost1) < step_size:
# break
# else:
# cost1 = cost2
cost2 = self.cost(X, y, self.coeffs)
if abs(cost2 - cost1) < step_size:
break
else:
cost1 = cost2
else:
print "step_size parameter must be initialized!"
for i in xrange(num_iterations):
np.random.shuffle(X)
for i, x in enumerate(X):
self.coeffs = self.coeffs + alpha * self.gradient(
x, y[i], self.coeffs, l=1)
def _modify_matrix(self, X):
if self.scale:
X = (X - self.sigma) / self.mu
if self.fit_intercept:
X = add_intercept(X)
return X
def _modify_matrix(self, X):
if self.scale:
X = (X - self.sigma) / self.mu
if self.fit_intercept:
X = add_intercept(X)
return X