def np_cov(m, rowvar=False):
# Handles complex arrays too
# m = np.asarray(m)
if m.ndim > 2:
raise ValueError('m has more than 2 dimensions')
dtype = np.result_type(m, np.float64)
m = np.array(m, ndmin=2, dtype=dtype)
if not rowvar and m.shape[0] != 1:
m = m.T
if m.shape[0] == 0:
return np.array([], dtype=dtype).reshape(0, 0)
# Determine the normalization
fact = m.shape[1] - 1
if fact <= 0:
warnings.warn("Degrees of freedom <= 0 for slice",
RuntimeWarning,
stacklevel=2)
fact = 0.0
m -= np.mean(m, axis=1, keepdims=1)
# c = np.dot(m, m.T.conj())
c = np.dot(m, m.T)
c *= np.true_divide(1, fact)
return c.squeeze()
def make_grad_logsumexp(ans, x, axis=None, b=1.0, keepdims=False):
shape, dtype = np.shape(x), np.result_type(x)
def vjp(g):
g_repeated, _ = repeat_to_match_shape(g, shape, dtype, axis, keepdims)
ans_repeated, _ = repeat_to_match_shape(ans, shape, dtype, axis, keepdims)
return g_repeated * b * np.exp(x - ans_repeated)
return vjp
def make_grad_logsumexp(ans, x, axis=None, b=1.0, keepdims=False):
shape, dtype = anp.shape(x), anp.result_type(x)
def vjp(g):
g_repeated, _ = repeat_to_match_shape(g, shape, dtype, axis, keepdims)
ans_repeated, _ = repeat_to_match_shape(ans, shape, dtype, axis, keepdims)
return g_repeated * b * anp.exp(x - ans_repeated)
return vjp
def maybe_multiply(x, y):
if _is_constant_zero(x) or _is_constant_zero(y):
return np.zeros(np.broadcast(x, y).shape, dtype=np.result_type(x, y))
if _is_constant_one(x) and np.shape(y) == np.broadcast(x, y).shape:
return y
if _is_constant_one(y) and np.shape(x) == np.broadcast(x, y).shape:
return x
return _multiply_as_einsum(x, y)
def _distribute_einsum(formula, op, add_args, args1, args2):
# Make sure any implicit broadcasting isn't lost.
broadcast_shape = np.broadcast(*add_args).shape
dtype = np.result_type(*add_args)
add_args = [
arg * np.ones(broadcast_shape, dtype=dtype)
if not hasattr(arg, 'shape') or broadcast_shape != arg.shape else arg
for arg in add_args
]
return op(
*[np.einsum(formula, *(args1 + (arg, ) + args2)) for arg in add_args])
def _zeros_like_einsum(formula, args1, args2):
args = args1 + args2
input_formulas, output_formula = split_einsum_formula(formula)
input_formulas = input_formulas[:len(args1)] + input_formulas[len(args1) +
1:]
out_shape = []
for output_index in output_formula:
for i, input_formula in enumerate(input_formulas):
position = input_formula.find(output_index)
if position != -1:
out_shape.append(args[i].shape[position])
break
return np.zeros(out_shape, dtype=np.result_type(*args))
def _zeros_like_einsum(formula, args1, args2):
args = args1 + args2
input_formulas, output_formula = split_einsum_formula(formula)
output_formula = decompose_formula(output_formula)
input_formulas = input_formulas[:len(args1)] + input_formulas[len(args1) +
1:]
input_formulas = [
decompose_formula(input_formula) for input_formula in input_formulas
]
out_shape = []
for output_index in output_formula:
for i, input_formula in enumerate(input_formulas):
position = input_formula.index(output_index)
if position != -1 and output_index != '...':
out_shape.append(args[i].shape[position])
break
elif position != -1 and output_index == '...':
for offset in range(args[i].ndim - len(input_formula) + 1):
out_shape.append(args[i].shape[position + offset])
return np.zeros(out_shape, dtype=np.result_type(*args))
