def _wrapper(self, coef, intercept, loss_function, penalty_type, alpha, C,
l1_ratio, dataset, n_iter, fit_intercept, verbose, shuffle,
seed, pos_weight, neg_weight, learning_rate_type, eta0,
power_t, t_, intercept_decay, coefArray, interceptArray, i):
print("Start %s" % i)
coef, intercept = plain_sgd(coef, intercept, loss_function,
penalty_type, alpha, C, l1_ratio, dataset,
n_iter, int(fit_intercept), int(verbose),
int(shuffle), seed, pos_weight, neg_weight,
learning_rate_type, eta0, power_t, t_,
intercept_decay)
print(coef, intercept)
coefArray[i] = int(coef[0])
coefArray[i + 10] = int(coef[1])
coefArray[i + 20] = int(coef[2])
interceptArray[i] = int(intercept)
print("End %s" % i)
def _wrapper(self,coef, intercept, loss_function,
penalty_type, alpha, C, l1_ratio,
dataset, n_iter, fit_intercept,
verbose, shuffle, seed,
pos_weight, neg_weight,
learning_rate_type, eta0,
power_t, t_, intercept_decay,coefArray,interceptArray,i):
print("Start %s"%i)
coef,intercept = plain_sgd(coef, intercept, loss_function,
penalty_type, alpha, C, l1_ratio,
dataset, n_iter, int(fit_intercept),
int(verbose), int(shuffle), seed,
pos_weight, neg_weight,
learning_rate_type, eta0,
power_t, t_, intercept_decay)
print(coef, intercept)
coefArray[i] = int(coef[0])
coefArray[i+10] =int(coef[1])
coefArray[i+20] =int(coef[2])
interceptArray[i] = int(intercept)
print("End %s"%i)
def _fit_regressor(self, X, y, alpha, C, loss, learning_rate,
sample_weight, n_iter):
dataset, intercept_decay = make_dataset(X, y, sample_weight)
loss_function = self._get_loss_function(loss)
penalty_type = self._get_penalty_type(self.penalty)
learning_rate_type = self._get_learning_rate_type(learning_rate)
if self.t_ is None:
self.t_ = 1.0
random_state = check_random_state(self.random_state)
# numpy mtrand expects a C long which is a signed 32 bit integer under
# Windows
seed = random_state.randint(0, np.iinfo(np.int32).max)
if self.average > 0:
self.standard_coef_, self.standard_intercept_, \
self.average_coef_, self.average_intercept_ =\
average_sgd(self.standard_coef_,
self.standard_intercept_[0],
self.average_coef_,
self.average_intercept_[0],
loss_function,
penalty_type,
alpha, C,
self.l1_ratio,
dataset,
n_iter,
int(self.fit_intercept),
int(self.verbose),
int(self.shuffle),
seed,
1.0, 1.0,
learning_rate_type,
self.eta0, self.power_t, self.t_,
intercept_decay, self.average)
self.average_intercept_ = np.atleast_1d(self.average_intercept_)
self.standard_intercept_ = np.atleast_1d(self.standard_intercept_)
self.t_ += n_iter * X.shape[0]
if self.average <= self.t_ - 1.0:
self.coef_ = self.average_coef_
self.intercept_ = self.average_intercept_
else:
self.coef_ = self.standard_coef_
self.intercept_ = self.standard_intercept_
else:
self.coef_, self.intercept_ = \
plain_sgd(self.coef_,
self.intercept_[0],
loss_function,
penalty_type,
alpha, C,
self.l1_ratio,
dataset,
n_iter,
int(self.fit_intercept),
int(self.verbose),
int(self.shuffle),
seed,
1.0, 1.0,
learning_rate_type,
self.eta0, self.power_t, self.t_,
intercept_decay)
self.t_ += n_iter * X.shape[0]
self.intercept_ = np.atleast_1d(self.intercept_)