def test_init_empty(self):
# self.isinit_sheet_success(Frame(), [], (0, 0), [], None, [])
self.isinit_sheet_success(SeriesSet(nan=None), OrderedDict(), (0, 0),
[], None, [])
# self.isinit_sheet_success(Frame(columns=['A', 'B']), [], (0, 2), ['A', 'B'], None, [0, 0])
self.isinit_sheet_success(SeriesSet(columns=['A', 'B'], nan=None),
OrderedDict(A=Series([]), B=Series([])),
(0, 2), ['A', 'B'], None, [0, 0])
def __init__(self, engine, learn_rate, l1_penalty, l2_penalty):
BaseEngineModel.__init__(self, engine)
self.learn_rate = learn_rate
self.l1_penalty = l1_penalty
self.l2_penalty = l2_penalty
self._activator = Activators(self.engine)
self._accuracy = None
self._cost_history = Series() # Mistake Recorder
def transform(self,
X_mat,
stochastic_matrix=None,
min_error=0.0001,
max_iter=1000):
X_mat = self._mat(X_mat).T
if stochastic_matrix is False:
weight = self._weight
self._weight = weight = self._mat(stochastic_matrix)
assert isinstance(max_iter, int) and max_iter >= 1
assert X_mat.shape[1] == 1, 'X should be 1-D sequence'
assert X_mat.shape[0] == weight.shape[
1], 'items in the X not fit the shape of weight matrix'
for round_ in range(max_iter):
X_next = self._alpha * self._dot(
weight, X_mat) + (1.0 - self._alpha) / X_mat.shape[0]
error = self._sum(self._abs(X_next - X_mat))
X_mat = X_next
if error < min_error:
LogInfo(' Early stopped iteration')
break
return Series(X_mat.T.tolist()[0])
def predict(self, X):
assert X.shape[1] == self.n_features
return Series(self.predict_once(row) for row in X)