def train(self, func, x0):
"""Optimize parameters to minimze loss.
Arguments:
- func: A function of the parameters that returns a tuple with the gradient and the loss respectively.
- x0: Parameters to use as starting point.
Returns the parameters that minimize the loss.
"""
if self._uW is None:
# TODO: should we not refcast here?
self._uW = numx.zeros_like(x0)
self.deltaW = numx.ones_like(x0) * self.deltainit
updated_params = x0.copy()
for _ in range(self.epochs):
# TODO: properly name variables
uW = func(updated_params)[0]
WW = self._uW * uW
self.deltaW *= self.etaplus * (WW > 0) + self.etamin * (
WW < 0) + 1 * (WW == 0)
self.deltaW = numx.maximum(self.deltaW, self.deltamin)
self.deltaW = numx.minimum(self.deltaW, self.deltamax)
updated_params -= self.deltaW * numx.sign(uW)
self._uW = uW
return updated_params
def train(self, func, x0):
"""Optimize parameters to minimze loss.
Arguments:
- func: A function of the parameters that returns a tuple with the gradient and the loss respectively.
- x0: Parameters to use as starting point.
Returns the parameters that minimize the loss.
"""
if self._uW is None:
# TODO: should we not refcast here?
self._uW = numx.zeros_like(x0)
self.deltaW = numx.ones_like(x0) * self.deltainit
updated_params = x0.copy()
for _ in range(self.epochs):
# TODO: properly name variables
uW = func(updated_params)[0]
WW = self._uW * uW;
self.deltaW *= self.etaplus * (WW > 0) + self.etamin * (WW < 0) + 1 * (WW == 0);
self.deltaW = numx.maximum(self.deltaW, self.deltamin)
self.deltaW = numx.minimum(self.deltaW, self.deltamax)
updated_params -= self.deltaW * numx.sign(uW)
self._uW = uW
return updated_params
def _label(self, x):
"""
:param x: A matrix having different variables on different columns
and observations on the rows.
:type x: numpy.ndarray
:return: An array with class labels from the perceptron.
:rtype: numpy.ndarray
"""
# todo: consider iterables
return numx.sign(numx.dot(x, self.weights) + self.offset_weight)
def _label(self, words):
"""Classifies the words.
"""
# clear input dim hack
self._set_input_dim(None)
p = self._prob(words)
p_spam_W = p[-1]
p_nospam_W = p[1]
try:
q = p_spam_W / p_nospam_W
except ZeroDivisionError:
return 1
return -numx.sign(q - 1)
def _label(self, words):
"""Classifies the words.
"""
# clear input dim hack
self._set_input_dim(None)
p = self._prob(words)
p_spam_W = p[-1]
p_nospam_W = p[1]
try:
q = p_spam_W / p_nospam_W
except ZeroDivisionError:
return 1
return -numx.sign(q - 1)
def _label_one(self, pattern, threshold):
pattern = mdp.utils.bool_to_sign(pattern)
has_converged = False
while not has_converged:
has_converged = True
iter_order = range(len(self._weight_matrix))
if self._shuffled_update:
numx_rand.shuffle(iter_order)
for row in iter_order:
w_row = self._weight_matrix[row]
thresh_row = threshold[row]
new_pattern_row = numx.sign(numx.dot(w_row, pattern) - thresh_row)
if new_pattern_row == 0:
# Following McKay, Neural Networks, we do nothing
# when the new pattern is zero
pass
elif pattern[row] != new_pattern_row:
has_converged = False
pattern[row] = new_pattern_row
return mdp.utils.sign_to_bool(pattern)
def _label_one(self, pattern, threshold):
pattern = mdp.utils.bool_to_sign(pattern)
has_converged = False
while not has_converged:
has_converged = True
iter_order = range(len(self._weight_matrix))
if self._shuffled_update:
numx_rand.shuffle(iter_order)
for row in iter_order:
w_row = self._weight_matrix[row]
thresh_row = threshold[row]
new_pattern_row = numx.sign(
numx.dot(w_row, pattern) - thresh_row)
if new_pattern_row == 0:
# Following McKay, Neural Networks, we do nothing
# when the new pattern is zero
pass
elif pattern[row] != new_pattern_row:
has_converged = False
pattern[row] = new_pattern_row
return mdp.utils.sign_to_bool(pattern)
def _label(self, x):
"""Returns an array with class labels from the perceptron.
"""
return numx.sign(numx.dot(x, self.weights) + self.offset_weight)
def _label(self, x):
ret = [xi.sum() for xi in x]
return numx.sign(ret)
def _label(self, x):
"""Returns an array with class labels from the perceptron.
"""
return numx.sign(numx.dot(x, self.weights) + self.offset_weight)
def _label(self, x):
ret = [xi.sum() for xi in x]
return numx.sign(ret)
