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)
