def fit(self,
initial_theta,
stochastic=False,
adapt=False,
batch_size=1,
learning_rate=0.1,
momentum=0.9,
epoch=1000,
threshold=1e-4,
regularization_lambda=1.0):
if stochastic:
(result, costs) = Trainer.batch_gradient_descent(
learning_rate=0.05,
apapt=adapt,
batch_size=batch_size,
epoch=epoch,
costFunction=self.costFunctionReg,
theta=initial_theta,
X=self.X,
Y=self.Y,
l=0.0)
else:
(result, costs) = Trainer.gradient_descent(
maxiter=epoch,
learning_rate=0.5,
momentum=momentum,
threshold=threshold,
costFunction=self.costFunctionReg,
theta=initial_theta,
X=self.X,
Y=self.Y,
l=regularization_lambda)
self.trained_theta = result['theta']
return result, costs
def fit_multi_class(self, initial_theta, stochastic=False, adapt=False, batch_size=1, learning_rate=0.1,
momentum=0.0,
epoch=1000, threshold=1e-4, regularization_lambda=1.0):
all_theta = np.matrix(np.zeros((len(initial_theta), len(self.labels))))
if stochastic:
col = 0;
for c in self.labels:
Yc = (self.Y == c).astype(float)
(result, cost) = Trainer.batch_gradient_descent(learning_rate=learning_rate, adapt=adapt,
batch_size=batch_size, epoch=epoch,
costFunction=self.costFunctionReg, theta=initial_theta,
X=self.X, Y=Yc, l=0.0)
all_theta[:, col] = result['theta']
col += 1
self.trained_theta = all_theta
else:
col = 0;
for c in self.labels:
Yc = (self.Y == c).astype(float)
(result, cost) = Trainer.gradient_descent(maxiter=epoch, learning_rate=learning_rate, momentum=momentum,
threshold=threshold, costFunction=self.costFunctionReg,
theta=initial_theta, X=self.X, Y=Yc, l=regularization_lambda)
all_theta[:, col] = result['theta']
col += 1
self.trained_theta = all_theta
return all_theta
def fit(self, initial_theta, stochastic=False, adapt=False, batch_size=1, learning_rate=0.1, momentum=0.9,
epoch=1000, threshold=1e-4, regularization_lambda=1.0):
if stochastic:
(result, costs) = Trainer.batch_gradient_descent(learning_rate=0.05, apapt=adapt, batch_size=batch_size,
epoch=epoch, costFunction=self.costFunctionReg,
theta=initial_theta, X=self.X, Y=self.Y, l=0.0)
else:
(result, costs) = Trainer.gradient_descent(maxiter=epoch, learning_rate=0.5, momentum=momentum,
threshold=threshold, costFunction=self.costFunctionReg,
theta=initial_theta, X=self.X, Y=self.Y, l=regularization_lambda)
self.trained_theta = result['theta']
return result, costs
def fit_multi_class(self,
initial_theta,
stochastic=False,
adapt=False,
batch_size=1,
learning_rate=0.1,
momentum=0.0,
epoch=1000,
threshold=1e-4,
regularization_lambda=1.0):
all_theta = np.matrix(np.zeros((len(initial_theta), len(self.labels))))
if stochastic:
col = 0
for c in self.labels:
Yc = (self.Y == c).astype(float)
(result, cost) = Trainer.batch_gradient_descent(
learning_rate=learning_rate,
adapt=adapt,
batch_size=batch_size,
epoch=epoch,
costFunction=self.costFunctionReg,
theta=initial_theta,
X=self.X,
Y=Yc,
l=0.0)
all_theta[:, col] = result['theta']
col += 1
self.trained_theta = all_theta
else:
col = 0
for c in self.labels:
Yc = (self.Y == c).astype(float)
(result, cost) = Trainer.gradient_descent(
maxiter=epoch,
learning_rate=learning_rate,
momentum=momentum,
threshold=threshold,
costFunction=self.costFunctionReg,
theta=initial_theta,
X=self.X,
Y=Yc,
l=regularization_lambda)
all_theta[:, col] = result['theta']
col += 1
self.trained_theta = all_theta
return all_theta