def test_bincountOp(self):
w = T.vector('w')
for dtype in ('int8', 'int16', 'int32', 'int64',
'uint8', 'uint16', 'uint32', 'uint64'):
# uint64 always fails
# int64 and uint32 also fail if python int are 32-bit
int_bitwidth = theano.gof.python_int_bitwidth()
if int_bitwidth == 64:
numpy_unsupported_dtypes = ('uint64',)
if int_bitwidth == 32:
numpy_unsupported_dtypes = ('uint32', 'int64', 'uint64')
x = T.vector('x', dtype=dtype)
if dtype in numpy_unsupported_dtypes:
self.assertRaises(TypeError, bincount, x)
else:
a = np.random.random_integers(50, size=(25)).astype(dtype)
weights = np.random.random((25,)).astype(config.floatX)
f1 = theano.function([x], bincount(x))
f2 = theano.function([x, w], bincount(x, weights=w))
assert (np.bincount(a) == f1(a)).all()
assert np.allclose(np.bincount(a, weights=weights),
f2(a, weights))
if not numpy_16:
continue
f3 = theano.function([x], bincount(x, minlength=23))
f4 = theano.function([x], bincount(x, minlength=5))
assert (np.bincount(a, minlength=23) == f3(a)).all()
assert (np.bincount(a, minlength=5) == f4(a)).all()
def test_bincountOp(self):
w = T.vector('w')
for dtype in ('int8', 'int16', 'int32', 'int64', 'uint8', 'uint16',
'uint32', 'uint64'):
# uint64 always fails
# int64 and uint32 also fail if python int are 32-bit
int_bitwidth = theano.gof.python_int_bitwidth()
if int_bitwidth == 64:
numpy_unsupported_dtypes = ('uint64', )
if int_bitwidth == 32:
numpy_unsupported_dtypes = ('uint32', 'int64', 'uint64')
x = T.vector('x', dtype=dtype)
if dtype in numpy_unsupported_dtypes:
self.assertRaises(TypeError, bincount, x)
else:
a = np.random.random_integers(50, size=(25)).astype(dtype)
weights = np.random.random((25, )).astype(config.floatX)
f1 = theano.function([x], bincount(x))
f2 = theano.function([x, w], bincount(x, weights=w))
assert (np.bincount(a) == f1(a)).all()
assert np.allclose(np.bincount(a, weights=weights),
f2(a, weights))
if not numpy_16:
continue
f3 = theano.function([x], bincount(x, minlength=23))
f4 = theano.function([x], bincount(x, minlength=5))
assert (np.bincount(a, minlength=23) == f3(a)).all()
assert (np.bincount(a, minlength=5) == f4(a)).all()
def test_bincountFn(self):
w = T.vector("w")
for dtype in ("int8", "int16", "int32", "int64", "uint8", "uint16", "uint32", "uint64"):
x = T.vector("x", dtype=dtype)
# uint64 always fails
# int64 and uint32 also fail if python int are 32-bit
int_bitwidth = theano.gof.python_int_bitwidth()
if int_bitwidth == 64:
numpy_unsupported_dtypes = ("uint64",)
if int_bitwidth == 32:
numpy_unsupported_dtypes = ("uint32", "int64", "uint64")
# uint64 always fails
if dtype in numpy_unsupported_dtypes:
self.assertRaises(TypeError, bincount, x)
else:
a = np.random.random_integers(50, size=(25)).astype(dtype)
weights = np.random.random((25,)).astype(config.floatX)
f1 = theano.function([x], bincount(x))
f2 = theano.function([x, w], bincount(x, weights=w))
assert (np.bincount(a) == f1(a)).all()
assert np.allclose(np.bincount(a, weights=weights), f2(a, weights))
f3 = theano.function([x], bincount(x, minlength=23))
f4 = theano.function([x], bincount(x, minlength=5))
assert (np.bincount(a, minlength=23) == f3(a)).all()
assert (np.bincount(a, minlength=5) == f4(a)).all()
# skip the following test when using unsigned ints
if not dtype.startswith("u"):
a[0] = -1
f5 = theano.function([x], bincount(x, assert_nonneg=True))
self.assertRaises(AssertionError, f5, a)
def test_bincountFn(self):
w = T.vector('w')
for dtype in ('int8', 'int16', 'int32', 'int64',
'uint8', 'uint16', 'uint32', 'uint64'):
x = T.vector('x', dtype=dtype)
# uint64 always fails
if dtype in ('uint64',):
self.assertRaises(TypeError, bincount, x)
else:
a = np.random.random_integers(50, size=(25)).astype(dtype)
weights = np.random.random((25,)).astype(config.floatX)
f1 = theano.function([x], bincount(x))
f2 = theano.function([x, w], bincount(x, weights=w))
assert (np.bincount(a) == f1(a)).all()
assert np.allclose(np.bincount(a, weights=weights),
f2(a, weights))
f3 = theano.function([x], bincount(x, minlength=23))
f4 = theano.function([x], bincount(x, minlength=5))
assert (np.bincount(a, minlength=23) == f3(a)).all()
assert (np.bincount(a, minlength=5) == f4(a)).all()
# skip the following test when using unsigned ints
if not dtype.startswith('u'):
a[0] = -1
f5 = theano.function([x], bincount(x, assert_nonneg=True))
self.assertRaises(AssertionError, f5, a)
def test_bincountFn(self):
w = T.vector("w")
def ref(data, w=None, minlength=None):
size = int(data.max() + 1)
if minlength:
size = max(size, minlength)
if w is not None:
out = np.zeros(size, dtype=w.dtype)
for i in range(data.shape[0]):
out[data[i]] += w[i]
else:
out = np.zeros(size, dtype=a.dtype)
for i in range(data.shape[0]):
out[data[i]] += 1
return out
for dtype in (
"int8",
"int16",
"int32",
"int64",
"uint8",
"uint16",
"uint32",
"uint64",
):
x = T.vector("x", dtype=dtype)
a = np.random.randint(1, 51, size=(25)).astype(dtype)
weights = np.random.random((25, )).astype(config.floatX)
f1 = theano.function([x], bincount(x))
f2 = theano.function([x, w], bincount(x, weights=w))
assert (ref(a) == f1(a)).all()
assert np.allclose(ref(a, weights), f2(a, weights))
f3 = theano.function([x], bincount(x, minlength=55))
f4 = theano.function([x], bincount(x, minlength=5))
assert (ref(a, minlength=55) == f3(a)).all()
assert (ref(a, minlength=5) == f4(a)).all()
# skip the following test when using unsigned ints
if not dtype.startswith("u"):
a[0] = -1
f5 = theano.function([x], bincount(x, assert_nonneg=True))
with pytest.raises(AssertionError):
f5(a)
def test_infer_shape(self):
for dtype in tensor.discrete_dtypes:
# uint64 always fails
# int64 and uint32 also fail if python int are 32-bit
int_bitwidth = theano.gof.python_int_bitwidth()
if int_bitwidth == 64:
numpy_unsupported_dtypes = ('uint64',)
if int_bitwidth == 32:
numpy_unsupported_dtypes = ('uint32', 'int64', 'uint64')
x = T.vector('x', dtype=dtype)
if dtype in numpy_unsupported_dtypes:
self.assertRaises(TypeError, bincount, x)
else:
self._compile_and_check(
[x],
[bincount(x)],
[np.random.random_integers(
50, size=(25,)).astype(dtype)],
self.op_class)
weights = np.random.random((25,)).astype(config.floatX)
self._compile_and_check(
[x],
[bincount(x, weights=weights)],
[np.random.random_integers(
50, size=(25,)).astype(dtype)],
self.op_class)
if not numpy_16:
continue
self._compile_and_check(
[x],
[bincount(x, minlength=60)],
[np.random.random_integers(
50, size=(25,)).astype(dtype)],
self.op_class)
self._compile_and_check(
[x],
[bincount(x, minlength=5)],
[np.random.random_integers(
50, size=(25,)).astype(dtype)],
self.op_class)
def test_infer_shape(self):
for dtype in tensor.discrete_dtypes:
# uint64 always fails
# int64 and uint32 also fail if python int are 32-bit
int_bitwidth = theano.gof.python_int_bitwidth()
if int_bitwidth == 64:
numpy_unsupported_dtypes = ('uint64',)
if int_bitwidth == 32:
numpy_unsupported_dtypes = ('uint32', 'int64', 'uint64')
x = T.vector('x', dtype=dtype)
if dtype in numpy_unsupported_dtypes:
self.assertRaises(TypeError, bincount, x)
else:
self._compile_and_check(
[x],
[bincount(x)],
[np.random.random_integers(
50, size=(25,)).astype(dtype)],
self.op_class)
weights = np.random.random((25,)).astype(config.floatX)
self._compile_and_check(
[x],
[bincount(x, weights=weights)],
[np.random.random_integers(
50, size=(25,)).astype(dtype)],
self.op_class)
if not numpy_16:
continue
self._compile_and_check(
[x],
[bincount(x, minlength=60)],
[np.random.random_integers(
50, size=(25,)).astype(dtype)],
self.op_class)
self._compile_and_check(
[x],
[bincount(x, minlength=5)],
[np.random.random_integers(
50, size=(25,)).astype(dtype)],
self.op_class)
def test_bincountFn(self):
w = T.vector('w')
def ref(data, w=None, minlength=None):
size = data.max() + 1
if minlength:
size = max(size, minlength)
if w is not None:
out = np.zeros(size, dtype=w.dtype)
for i in range(data.shape[0]):
out[data[i]] += w[i]
else:
out = np.zeros(size, dtype=a.dtype)
for i in range(data.shape[0]):
out[data[i]] += 1
return out
for dtype in ('int8', 'int16', 'int32', 'int64', 'uint8', 'uint16',
'uint32', 'uint64'):
x = T.vector('x', dtype=dtype)
a = np.random.random_integers(50, size=(25)).astype(dtype)
weights = np.random.random((25, )).astype(config.floatX)
f1 = theano.function([x], bincount(x))
f2 = theano.function([x, w], bincount(x, weights=w))
assert (ref(a) == f1(a)).all()
assert np.allclose(ref(a, weights), f2(a, weights))
f3 = theano.function([x], bincount(x, minlength=55))
f4 = theano.function([x], bincount(x, minlength=5))
assert (ref(a, minlength=55) == f3(a)).all()
assert (ref(a, minlength=5) == f4(a)).all()
# skip the following test when using unsigned ints
if not dtype.startswith('u'):
a[0] = -1
f5 = theano.function([x], bincount(x, assert_nonneg=True))
self.assertRaises(AssertionError, f5, a)
def test_bincountFn(self):
w = T.vector('w')
def ref(data, w=None, minlength=None):
size = int(data.max() + 1)
if minlength:
size = max(size, minlength)
if w is not None:
out = np.zeros(size, dtype=w.dtype)
for i in range(data.shape[0]):
out[data[i]] += w[i]
else:
out = np.zeros(size, dtype=a.dtype)
for i in range(data.shape[0]):
out[data[i]] += 1
return out
for dtype in ('int8', 'int16', 'int32', 'int64',
'uint8', 'uint16', 'uint32', 'uint64'):
x = T.vector('x', dtype=dtype)
a = np.random.randint(1, 51, size=(25)).astype(dtype)
weights = np.random.random((25,)).astype(config.floatX)
f1 = theano.function([x], bincount(x))
f2 = theano.function([x, w], bincount(x, weights=w))
assert (ref(a) == f1(a)).all()
assert np.allclose(ref(a, weights), f2(a, weights))
f3 = theano.function([x], bincount(x, minlength=55))
f4 = theano.function([x], bincount(x, minlength=5))
assert (ref(a, minlength=55) == f3(a)).all()
assert (ref(a, minlength=5) == f4(a)).all()
# skip the following test when using unsigned ints
if not dtype.startswith('u'):
a[0] = -1
f5 = theano.function([x], bincount(x, assert_nonneg=True))
self.assertRaises(AssertionError, f5, a)
def computeC(self, S):
M = self.M
n_step_sizes = len(self.step_sizes)
obs_jump_ind = TT.as_tensor_variable(self.obs_jump_ind, 'obs_jump_ind')
tau_ind = obs_jump_ind[1:] * M * M
#tau_ind = obs_jump_ind[:-1]*M*M
#tau_ind = TT.flatten(obs_jump_ind)[:-1]*M*M
keep_jumps = (tau_ind >= 0).nonzero()
jump_from_ind = S[:-1] * M
jump_to_ind = S[1:]
#jump_from_ind = TT.flatten(S)[:-1]*M
#jump_to_ind = TT.flatten(S)[1:]
#import pdb; pdb.set_trace()
flat_ind = (tau_ind + jump_from_ind + jump_to_ind)[keep_jumps]
flat_ind_counts = bincount(flat_ind, minlength=n_step_sizes * M * M)
C = flat_ind_counts.reshape(shape=np.array([n_step_sizes, M, M]))
return C
def computeC(self,S):
M = self.M
n_step_sizes = len(self.step_sizes)
obs_jump_ind = TT.as_tensor_variable(self.obs_jump_ind, 'obs_jump_ind')
tau_ind = obs_jump_ind[1:]*M*M
#tau_ind = obs_jump_ind[:-1]*M*M
#tau_ind = TT.flatten(obs_jump_ind)[:-1]*M*M
keep_jumps = (tau_ind >= 0).nonzero()
jump_from_ind = S[:-1]*M
jump_to_ind = S[1:]
#jump_from_ind = TT.flatten(S)[:-1]*M
#jump_to_ind = TT.flatten(S)[1:]
#import pdb; pdb.set_trace()
flat_ind = (tau_ind + jump_from_ind + jump_to_ind)[keep_jumps]
flat_ind_counts = bincount(flat_ind, minlength=n_step_sizes*M*M)
C = flat_ind_counts.reshape(shape=np.array([n_step_sizes,M,M]))
return C
