def test_check_input_arrays():
with raises(ValueError):
check_input_arrays(np.random.rand(10, 5), np.random.rand(5, 4))
with raises(ValueError):
check_input_arrays(np.random.rand(10), np.random.rand(5))
def __call__(self, x, y):
"""Actual implementation of kernel func"""
if not self.skip_input_checks:
x, y = check_input_arrays(x, y, ensure_dtype=np.number)
return (self.b + self.gamma * np.dot(x, y))**self.degree
def __call__(self, x, y):
"""Actual implementation of kernel func"""
if not self.skip_input_checks:
x, y = check_input_arrays(x, y, ensure_dtype=np.number)
return x.dot(y.T)
def __call__(self, x, y):
"""Actual implementation of kernel func"""
if not self.skip_input_checks:
x, y = check_input_arrays(x, y, ensure_dtype=np.number)
return np.exp(-self.gamma * np.sum(np.abs(x - y)))
def __call__(self, x, y):
"""Actual implementation of kernel func"""
if not self.skip_input_checks:
x, y = check_input_arrays(x, y, ensure_dtype=np.number)
return np.exp(-self.gamma * np.linalg.norm(x - y, ord=2)**2)
def __call__(self, x, y):
"""Actual implementation of kernel func"""
if not self.skip_input_checks:
x, y = check_input_arrays(x, y, ensure_dtype=np.number)
abs_x_a = np.power(np.abs(x), self.alpha)
abs_y_a = np.power(np.abs(y), self.alpha)
return np.dot((abs_x_a * abs_y_a), 2 * (abs_x_a + abs_y_a))
def __call__(self, x, y):
"""Actual implementation of kernel func"""
if not self.skip_input_checks:
x, y = check_input_arrays(x, y, ensure_dtype=np.float64)
if (x < 0).any() or (y < 0).any():
raise Chi2NegativeValuesException(
'Chi^2 kernel requires non-negative values!'
' x or y contains non-negative values')
# Note: NaNs due to Zero division are being ignored via np.nansum!
value = np.exp(-self.gamma * np.nansum(np.power(x - y, 2) / (x + y)))
return value
