def __init__(self, samples, labels, weights=None, scale=None): # list_X = [r + [1] for r in samples] list_X = [r for r in samples] self.X = array(list_X) self.Y = array(labels) self.weights = weights if scale is None: self.scale = Scale.unit_scale(len(list_X[0])) else: # self.scale = scale.add_unit_elements(1) self.scale = scale
def __init__(self, train_data, train_labels, weights=None, scale=None): # Adding 1 at the end for the constant term list_X = [r + [1] for r in train_data] if scale is None: self.scale = Scale.unit_scale(len(list_X[0])) else: self.scale = scale.add_unit_elements(1) self.weights = weights self.X = self.scale(np.array(list_X)) self.Y = np.array(train_labels)
def __init__(self, train_data, train_labels, weights=None, scale=None): # Adding 1 at the end for the constant term list_X = [r + [1] for r in train_data] if scale is None: self.scale = Scale.unit_scale(len(list_X[0])) else: self.scale = scale.add_unit_elements(1) self.weights = weights self.X = self.scale(np.array(list_X)) self.Y = np.array(train_labels)
def test_unit_scale(self): scale = Scale.unit_scale(3) a = np.array([1, 2, 3]) scaled_a = scale.scale(a) testing.assert_array_equal(a, scaled_a)
def test_mul(self): scale = Scale.unit_scale(3) two_scale = scale * 2 a = np.array([1, 2, 3]) scaled_a = two_scale.scale(a) testing.assert_array_equal(a * 2, scaled_a)