def test_MatrixSlice():
n = sympy.Symbol('n', integer=True)
X = sympy.MatrixSymbol('X', n, n)
Y = X[1:2:3, 4:5:6]
Yt = theano_code(Y)
from theano.scalar import Scalar
from theano import Constant
s = Scalar('int64')
assert tuple(Yt.owner.op.idx_list) == (slice(s, s, s), slice(s, s, s))
assert Yt.owner.inputs[0] == theano_code(X)
# == doesn't work in theano like it does in SymPy. You have to use
# equals.
assert [
i.equals(j) for i, j in zip(Yt.owner.inputs[1:], [
Constant(s, 1),
Constant(s, 2),
Constant(s, 3),
Constant(s, 4),
Constant(s, 5),
Constant(s, 6),
])
]
k = sympy.Symbol('k')
kt = theano_code(k, dtypes={k: 'int32'})
start, stop, step = 4, k, 2
Y = X[start:stop:step]
Yt = theano_code(Y, dtypes={n: 'int32', k: 'int32'})
def test_hash_and_eq_params(self):
wp1 = ParamsType(a=Generic(),
array=TensorType('int64', (False, )),
floatting=Scalar('float64'),
npy_scalar=TensorType('float64', tuple()))
wp2 = ParamsType(a=Generic(),
array=TensorType('int64', (False, )),
floatting=Scalar('float64'),
npy_scalar=TensorType('float64', tuple()))
w1 = Params(wp1,
a=1,
array=np.asarray([1, 2, 4, 5, 7]),
floatting=-4.5,
npy_scalar=np.asarray(12))
w2 = Params(wp2,
a=1,
array=np.asarray([1, 2, 4, 5, 7]),
floatting=-4.5,
npy_scalar=np.asarray(12))
assert w1 == w2
assert not (w1 != w2)
assert hash(w1) == hash(w2)
# Changing attributes names only (a -> other_name).
wp2_other = ParamsType(other_name=Generic(),
array=TensorType('int64', (False, )),
floatting=Scalar('float64'),
npy_scalar=TensorType('float64', tuple()))
w2 = Params(wp2_other,
other_name=1,
array=np.asarray([1, 2, 4, 5, 7]),
floatting=-4.5,
npy_scalar=np.asarray(12))
assert w1 != w2
# Changing attributes values only (now a=2).
w2 = Params(wp2,
a=2,
array=np.asarray([1, 2, 4, 5, 7]),
floatting=-4.5,
npy_scalar=np.asarray(12))
assert w1 != w2
# Changing NumPy array values (5 -> -5).
w2 = Params(wp2,
a=1,
array=np.asarray([1, 2, 4, -5, 7]),
floatting=-4.5,
npy_scalar=np.asarray(12))
assert w1 != w2
def make_node(self, *inputs):
inp = list(map(tensor.as_tensor_variable, inputs))
bc=[]
if self.dimension == None:
out = [Scalar(inp[0].type.dtype)('out')]
else:
for i in range(inp[0].ndim):
if i == self.dimension or inp[0].broadcastable[i] == True:
bc.append(True)
else:
bc.append(False)
out = [tensor.TensorType(inp[0].type.dtype, bc)()]
#if self.keepdim:
#else:
return gof.Apply(self, inp, out)
from __future__ import absolute_import, print_function, division
import theano
import numpy as np
from unittest import TestCase
from theano.gof import Op, COp, Apply
from theano import Generic
from theano.scalar import Scalar
from theano.tensor import TensorType
from theano.gof import ParamsType, Params, EnumList
from theano import tensor
from theano.tests import unittest_tools as utt
tensor_type_0d = TensorType('float64', tuple())
scalar_type = Scalar('float64')
generic_type = Generic()
# A test op to compute `y = a*x^2 + bx + c` for any tensor x, with a, b, c as op params.
class QuadraticOpFunc(Op):
__props__ = ('a', 'b', 'c')
params_type = ParamsType(a=tensor_type_0d, b=scalar_type, c=generic_type)
def __init__(self, a, b, c):
self.a = a
self.b = b
self.c = c
def make_node(self, x):
x = tensor.as_tensor_variable(x)
return Apply(self, [x], [x.type()])
import numpy as np
import pytest
import theano
from tests import unittest_tools as utt
from theano import Generic, tensor
from theano.gof import Apply, COp, EnumList, Op, Params, ParamsType
from theano.scalar import Scalar
from theano.tensor import TensorType
tensor_type_0d = TensorType("float64", tuple())
scalar_type = Scalar("float64")
generic_type = Generic()
# A test op to compute `y = a*x^2 + bx + c` for any tensor x, with a, b, c as op params.
class QuadraticOpFunc(Op):
__props__ = ("a", "b", "c")
params_type = ParamsType(a=tensor_type_0d, b=scalar_type, c=generic_type)
def __init__(self, a, b, c):
self.a = a
self.b = b
self.c = c
def make_node(self, x):
x = tensor.as_tensor_variable(x)
return Apply(self, [x], [x.type()])
def perform(self, node, inputs, output_storage, coefficients):
x = inputs[0]
def c_code(self, node, name, ins, outs, sub):
# support old pickled graphs
if len(ins) == 2:
(pvals, unis) = ins
n = 1
else:
(pvals, unis, n) = ins
(z,) = outs
if self.odtype == "auto":
t = f"PyArray_TYPE({pvals})"
else:
t = Scalar(self.odtype).dtype_specs()[1]
if t.startswith("theano_complex"):
t = t.replace("theano_complex", "NPY_COMPLEX")
else:
t = t.upper()
fail = sub["fail"]
return (
"""
if (PyArray_NDIM(%(pvals)s) != 2)
{
PyErr_Format(PyExc_TypeError, "pvals ndim should be 2");
%(fail)s;
}
if (PyArray_NDIM(%(unis)s) != 1)
{
PyErr_Format(PyExc_TypeError, "unis ndim should be 2");
%(fail)s;
}
if (PyArray_DIMS(%(unis)s)[0] != (PyArray_DIMS(%(pvals)s)[0] * %(n)s))
{
PyErr_Format(PyExc_ValueError, "unis.shape[0] != pvals.shape[0] * n");
%(fail)s;
}
if ((NULL == %(z)s)
|| ((PyArray_DIMS(%(z)s))[0] != (PyArray_DIMS(%(pvals)s))[0])
|| ((PyArray_DIMS(%(z)s))[1] != (PyArray_DIMS(%(pvals)s))[1])
)
{
Py_XDECREF(%(z)s);
%(z)s = (PyArrayObject*) PyArray_EMPTY(2,
PyArray_DIMS(%(pvals)s),
%(t)s,
0);
if (!%(z)s)
{
PyErr_SetString(PyExc_MemoryError, "failed to alloc z output");
%(fail)s;
}
}
{ // NESTED SCOPE
const int nb_multi = PyArray_DIMS(%(pvals)s)[0];
const int nb_outcomes = PyArray_DIMS(%(pvals)s)[1];
const int n_samples = %(n)s;
//
// For each multinomial, loop over each possible outcome
//
for (int c = 0; c < n_samples; ++c){
for (int n = 0; n < nb_multi; ++n)
{
int waiting = 1;
double cummul = 0.;
const dtype_%(unis)s* unis_n = (dtype_%(unis)s*)PyArray_GETPTR1(%(unis)s, c*nb_multi + n);
for (int m = 0; m < nb_outcomes; ++m)
{
dtype_%(z)s* z_nm = (dtype_%(z)s*)PyArray_GETPTR2(%(z)s, n,m);
const dtype_%(pvals)s* pvals_nm = (dtype_%(pvals)s*)PyArray_GETPTR2(%(pvals)s, n,m);
cummul += *pvals_nm;
if (c == 0)
{
if (waiting && (cummul > *unis_n))
{
*z_nm = 1.;
waiting = 0;
}
else
{
// if we re-used old z pointer, we have to clear it out.
*z_nm = 0.;
}
}
else {
if (cummul > *unis_n)
{
*z_nm = *z_nm + 1.;
break;
}
}
}
}
}
} // END NESTED SCOPE
"""
% locals()
)
def c_code(self, node, name, ins, outs, sub):
(pvals, unis, n) = ins
(z,) = outs
replace = int(self.replace)
if self.odtype == "auto":
t = "NPY_INT64"
else:
t = Scalar(self.odtype).dtype_specs()[1]
if t.startswith("theano_complex"):
t = t.replace("theano_complex", "NPY_COMPLEX")
else:
t = t.upper()
fail = sub["fail"]
return (
"""
// create a copy of pvals matrix
PyArrayObject* pvals_copy = NULL;
if (PyArray_NDIM(%(pvals)s) != 2)
{
PyErr_Format(PyExc_TypeError, "pvals ndim should be 2");
%(fail)s;
}
if (PyArray_NDIM(%(unis)s) != 1)
{
PyErr_Format(PyExc_TypeError, "unis ndim should be 2");
%(fail)s;
}
if ( %(n)s > (PyArray_DIMS(%(pvals)s)[1]) )
{
PyErr_Format(PyExc_ValueError, "Cannot sample without replacement n samples bigger than the size of the distribution.");
%(fail)s;
}
if (PyArray_DIMS(%(unis)s)[0] != (PyArray_DIMS(%(pvals)s)[0] * %(n)s))
{
PyErr_Format(PyExc_ValueError, "unis.shape[0] != pvals.shape[0] * n");
%(fail)s;
}
pvals_copy = (PyArrayObject*) PyArray_EMPTY(2,
PyArray_DIMS(%(pvals)s),
PyArray_TYPE(%(pvals)s),
0);
if (!pvals_copy)
{
PyErr_SetString(PyExc_MemoryError, "failed to alloc pvals_copy");
%(fail)s;
}
PyArray_CopyInto(pvals_copy, %(pvals)s);
if ((NULL == %(z)s)
|| ((PyArray_DIMS(%(z)s))[0] != (PyArray_DIMS(%(pvals)s))[0])
|| ((PyArray_DIMS(%(z)s))[1] != %(n)s)
)
{
Py_XDECREF(%(z)s);
npy_intp dims[2];
dims[0] = PyArray_DIMS(%(pvals)s)[0];
dims[1] = %(n)s;
%(z)s = (PyArrayObject*) PyArray_EMPTY(2,
dims,
%(t)s,
-1);
if (!%(z)s)
{
PyErr_SetString(PyExc_MemoryError, "failed to alloc z output");
%(fail)s;
}
}
{ // NESTED SCOPE
const int nb_multi = PyArray_DIMS(%(pvals)s)[0];
const int nb_outcomes = PyArray_DIMS(%(pvals)s)[1];
const int n_samples = %(n)s;
//
// For each multinomial, loop over each possible outcome,
// and set selected pval to 0 after being selected
//
for (int c = 0; c < n_samples; ++c){
for (int n = 0; n < nb_multi; ++n)
{
double cummul = 0.;
const dtype_%(unis)s* unis_n = (dtype_%(unis)s*)PyArray_GETPTR1(%(unis)s, c*nb_multi + n);
dtype_%(z)s* z_nc = (dtype_%(z)s*)PyArray_GETPTR2(%(z)s, n, c);
for (int m = 0; m < nb_outcomes; ++m)
{
dtype_%(pvals)s* pvals_nm = (dtype_%(pvals)s*)PyArray_GETPTR2(pvals_copy, n, m);
cummul += *pvals_nm;
if (cummul > *unis_n)
{
*z_nc = m;
// No need to renormalize after the last samples.
if (c == (n_samples - 1))
break;
if (! %(replace)s )
{
// renormalize the nth row of pvals, reuse (cummul-*pvals_nm) to initialize the sum
dtype_%(pvals)s sum = cummul - *pvals_nm;
dtype_%(pvals)s* pvals_n = (dtype_%(pvals)s*)PyArray_GETPTR2(pvals_copy, n, m);
*pvals_nm = 0.;
for (int k = m; k < nb_outcomes; ++k)
{
sum = sum + *pvals_n;
pvals_n++;
}
pvals_n = (dtype_%(pvals)s*)PyArray_GETPTR2(pvals_copy, n, 0);
for (int k = 0; k < nb_outcomes; ++k)
{
*pvals_n = *pvals_n / sum;
pvals_n++;
}
}
break;
}
}
}
}
// delete pvals_copy
{
Py_XDECREF(pvals_copy);
}
} // END NESTED SCOPE
"""
% locals()
)
