def test_float_exp_():
data = np.array([0., 1., 2., 5.])
expected = np.array([1., 2.71828183, 7.3890561, 148.4131591])
a = FloatTensor(data)
a.exp_()
# a does change when inlined
np.testing.assert_almost_equal(expected, a.to_numpy(), decimal=4)
def test_float_log1p_():
data = np.array([1.2, -0.9, 9.9, 0.1, -0.455])
expected = np.array([0.78845736, -2.30258509, 2.38876279, 0.09531018, -0.60696948])
a = FloatTensor(data)
a.log1p_()
# a does change when inlined
np.testing.assert_almost_equal(expected, a.to_numpy(), decimal=5)
def test_float_reciprocal_():
data = np.array([1., 2., 3., 4.])
expected = np.array([1., 0.5, 0.33333333, 0.25])
a = FloatTensor(data)
a.reciprocal_()
# a does change when inlined
np.testing.assert_almost_equal(expected, a.to_numpy(), decimal=4)
def test_float_round_():
data = np.array([12.7292, -3.11, 9.00, 20.4999, 20.5001])
expected = np.array([13, -3, 9, 20, 21])
a = FloatTensor(data)
a.round_()
# a does change when inlined
np.testing.assert_array_equal(expected, a.to_numpy())
def test_float_rsqrt_():
data = np.array([1., 2., 3., 4.])
expected = np.array([1., 0.7071068, 0.5773503, 0.5])
a = FloatTensor(data)
a.rsqrt_()
# a does change when inlined
np.testing.assert_almost_equal(expected, a.to_numpy(), decimal=4)
def test_float_sqrt_():
data = np.array([1., 2., 3., 4.])
expected = np.array([1., 1.41421356, 1.73205081, 2.])
a = FloatTensor(data)
a.sqrt_()
# a does change when inlined
np.testing.assert_almost_equal(expected, a.to_numpy(), decimal=4)
def test_float_exp_():
data = np.array([0, np.log(2)])
expected = np.array([1., 2.])
a = FloatTensor(data)
a.exp_()
# a does change when inlined
np.testing.assert_almost_equal(a.to_numpy(), expected,
decimal=decimal_accuracy, verbose=verbosity)
def test_float_erfinv_():
data = np.array([0, 0.5, -1.])
expected = np.array([0., 0.47693628, -np.inf])
a = FloatTensor(data)
a.erfinv_()
# a does change when inlined
np.testing.assert_almost_equal(a.to_numpy(), expected,
decimal=decimal_accuracy, verbose=verbosity)
def test_float_erf_():
data = np.array([0, -1., 10.])
expected = np.array([0., -0.84270078, 1.])
a = FloatTensor(data)
a.erf_()
# a does change when inlined
np.testing.assert_almost_equal(a.to_numpy(), expected,
decimal=decimal_accuracy, verbose=verbosity)
def test_float_cosh_():
data = np.array([-0.6366, 0.2718, 0.4469, 1.3122])
expected = np.array([1.209566, 1.03716552, 1.10153294, 1.99178159])
a = FloatTensor(data)
a.cosh_()
# a does change when inlined
np.testing.assert_almost_equal(a.to_numpy(), expected,
decimal=decimal_accuracy, verbose=verbosity)
def test_float_cos_():
data = np.array([-0.6366, 0.2718, 0.4469, 1.3122])
expected = np.array([0.80412155, 0.9632892, 0.90179116, 0.25572386])
a = FloatTensor(data)
a.cos_()
# a does change when inlined
np.testing.assert_almost_equal(a.to_numpy(), expected,
decimal=decimal_accuracy, verbose=verbosity)
def test_float_atan_():
data = np.array([-0.6366, 0.2718, 0.4469, 1.3122])
expected = np.array([-0.56689745, 0.26538879, 0.42027298, 0.91960937])
a = FloatTensor(data)
a.atan_()
# a does change when inlined
np.testing.assert_almost_equal(a.to_numpy(), expected,
decimal=decimal_accuracy, verbose=verbosity)
def test_float_asin_():
data = np.array([-0.6366, 0.2718, 0.4469, 1.3122])
expected = np.array([-0.69008148, 0.27526295, 0.46329704, np.nan])
a = FloatTensor(data)
a.asin_()
# a does change when inlined
np.testing.assert_almost_equal(a.to_numpy(), expected,
decimal=decimal_accuracy, verbose=verbosity)
def test_float_floor_():
data = np.array([1.3869, 0.3912, -0.8634, -0.5468])
expected = np.array([1., 0., -1., -1.])
a = FloatTensor(data)
a.floor_()
# a does change when inlined
np.testing.assert_almost_equal(a.to_numpy(), expected,
decimal=decimal_accuracy, verbose=verbosity)
def test_float_sqrt():
data = np.array([1., 2., 3., 4.])
expected = np.array([1., 1.41421356, 1.73205081, 2.])
a = FloatTensor(data)
b = a.sqrt()
np.testing.assert_almost_equal(expected, b.to_numpy(), decimal=4)
# a doesn't change
np.testing.assert_array_equal(data, a.to_numpy())
def test_float_acos_():
data = np.array([-1., -2., 3., 4., 5., -6.])
expected = np.array([1., 2., 3., 4., 5., 6.])
a = FloatTensor(data)
a.abs_()
# a does change when inlined
np.testing.assert_almost_equal(a.to_numpy(), expected,
decimal=decimal_accuracy, verbose=verbosity)
def test_float_rsqrt():
data = np.array([1., 2., 3., 4.])
expected = np.array([1., 0.7071068, 0.5773503, 0.5])
a = FloatTensor(data)
b = a.rsqrt()
np.testing.assert_almost_equal(expected, b.to_numpy(), decimal=4)
# a doesn't change
np.testing.assert_array_equal(data, a.to_numpy())
def test_float_round():
data = np.array([12.7292, -3.11, 9.00, 20.4999, 20.5001])
expected = np.array([13, -3, 9, 20, 21])
a = FloatTensor(data)
b = a.round()
np.testing.assert_array_equal(expected, b.to_numpy())
# a doesn't change
np.testing.assert_array_equal(data, a.to_numpy())
def test_float_reciprocal():
data = np.array([1., 2., 3., 4.])
expected = np.array([1., 0.5, 0.33333333, 0.25])
a = FloatTensor(data)
b = a.reciprocal()
np.testing.assert_almost_equal(expected, b.to_numpy(), decimal=4)
# a doesn't change
np.testing.assert_array_equal(data, a.to_numpy())
def test_float_exp():
data = np.array([0., 1., 2., 5.])
expected = np.array([1., 2.71828183, 7.3890561, 148.4131591])
a = FloatTensor(data)
b = a.exp()
np.testing.assert_almost_equal(expected, b.to_numpy(), decimal=4)
# a doesn't change
np.testing.assert_array_equal(data, a.to_numpy())
def test_float_fmod_():
data = np.array([-3, -2, -1, 1, 2, 3])
expected = np.array([-1., -0., -1., 1., 0., 1.])
a = FloatTensor(data)
a.fmod_(2)
# a does change when inlined
np.testing.assert_almost_equal(a.to_numpy(), expected,
decimal=decimal_accuracy, verbose=verbosity)
def test_float_fmod():
data = np.array([-3, -2, -1, 1, 2, 3])
expected = np.array([-1., -0., -1., 1., 0., 1.])
a = FloatTensor(data)
b = a.fmod(2)
np.testing.assert_almost_equal(b.to_numpy(), expected,
decimal=decimal_accuracy, verbose=verbosity)
# a doesn't change
np.testing.assert_almost_equal(a.to_numpy(), data,
decimal=decimal_accuracy, verbose=verbosity)
def test_float_ceil():
data = np.array([1.3869, 0.3912, -0.8634, -0.5468])
expected = np.array([2., 1., -0., -0.])
a = FloatTensor(data)
b = a.ceil()
np.testing.assert_almost_equal(b.to_numpy(), expected,
decimal=decimal_accuracy, verbose=verbosity)
# a doesn't change
np.testing.assert_almost_equal(a.to_numpy(), data,
decimal=decimal_accuracy, verbose=verbosity)
def test_float_erf():
data = np.array([0, -1., 10.])
expected = np.array([0., -0.84270078, 1.])
a = FloatTensor(data)
b = a.erf()
np.testing.assert_almost_equal(b.to_numpy(), expected,
decimal=decimal_accuracy, verbose=verbosity)
# a doesn't change
np.testing.assert_almost_equal(a.to_numpy(), data,
decimal=decimal_accuracy, verbose=verbosity)
def test_float_erfinv():
data = np.array([0, 0.5, -1.])
expected = np.array([0., 0.47693628, -np.inf])
a = FloatTensor(data)
b = a.erfinv()
np.testing.assert_almost_equal(b.to_numpy(), expected,
decimal=decimal_accuracy, verbose=verbosity)
# a doesn't change
np.testing.assert_almost_equal(a.to_numpy(), data,
decimal=decimal_accuracy, verbose=verbosity)
def test_float_exp():
data = np.array([0, np.log(2)])
expected = np.array([1., 2.])
a = FloatTensor(data)
b = a.exp()
np.testing.assert_almost_equal(b.to_numpy(), expected,
decimal=decimal_accuracy, verbose=verbosity)
# a doesn't change
np.testing.assert_almost_equal(a.to_numpy(), data,
decimal=decimal_accuracy, verbose=verbosity)
def test_float_acos():
data = np.array([-0.6366, 0.2718, 0.4469, 1.3122])
expected = np.array([2.26087785, 1.2955333, 1.10749924, np.nan])
a = FloatTensor(data)
b = a.acos()
np.testing.assert_almost_equal(b.to_numpy(), expected,
decimal=decimal_accuracy, verbose=verbosity)
# a doesn't change
np.testing.assert_almost_equal(a.to_numpy(), data,
decimal=decimal_accuracy, verbose=verbosity)
def test_float_abs():
data = np.array([-1., -2., 3., 4., 5., -6.])
expected = np.array([1., 2., 3., 4., 5., 6.])
a = FloatTensor(data)
b = a.abs()
np.testing.assert_almost_equal(b.to_numpy(), expected,
decimal=decimal_accuracy, verbose=verbosity)
# a doesn't change
np.testing.assert_almost_equal(a.to_numpy(), data,
decimal=decimal_accuracy, verbose=verbosity)
def test_float_trace():
data = np.random.randn(17, 17).astype('float')
expected = data.trace()
a = FloatTensor(data)
actual = a.trace()
np.testing.assert_almost_equal(actual, expected,
decimal=decimal_accuracy, verbose=verbosity)
a = a.gpu()
actual = a.trace()
np.testing.assert_almost_equal(actual, expected,
decimal=decimal_accuracy, verbose=verbosity)
def predict(self,x):
if(type(x) == list):
x = np.array(x).astype('float')
if(type(x) == np.array or type(x) == np.ndarray):
x = FloatTensor(x,autograd=True, delete_after_use=False)
return self.syft.forward(input=x).to_numpy()
