modified numpy imports to one common form

theano/sparse/sandbox/sp.py

@@ -8,7 +8,7 @@ U{http://www-users.cs.umn.edu/~saad/software/SPARSKIT/paper.ps}.
 """
 # COPIED FROM hpu/icml09/sp.py
 from __future__ import absolute_import, print_function, division
-import numpy
+import numpy as np
 import scipy
 from scipy import sparse as scipy_sparse
 from six.moves import xrange
@@ -81,18 +81,17 @@ class ConvolutionIndices(Op):
             raise Exception("ws is obsolete and it must be always True")
 
         (dx, dy) = strides
-        N = numpy
 
         # inshp contains either 2 entries (height,width) or 3 (nfeatures,h,w)
         # in the first case, default nfeatures to 1
-        if N.size(inshp) == 2:
+        if np.size(inshp) == 2:
             inshp = (1,) + inshp
 
-        inshp = N.array(inshp)
-        kshp = N.array(kshp)
-        ksize = N.prod(kshp)
+        inshp = np.array(inshp)
+        kshp = np.array(kshp)
+        ksize = np.prod(kshp)
 
-        kern = ksize - 1 - N.arange(ksize)
+        kern = ksize - 1 - np.arange(ksize)
 
         # size of output image if doing proper convolution
         # (mode='full',dx=dy=0) outshp is the actual output shape
@@ -102,32 +101,32 @@ class ConvolutionIndices(Op):
             s = -1
         else:
             s = 1
-        outshp = N.int64(N.ceil((inshp[1:] + s * kshp - s * 1) \
-                 / N.array([dy, dx], dtype='float')))
+        outshp = np.int64(np.ceil((inshp[1:] + s * kshp - s * 1) \
+                 / np.array([dy, dx], dtype='float')))
         if any(outshp <= 0):
             err = 'Invalid kernel', kshp, 'and/or step size', (dx, dy),\
                   'for given input shape', inshp
             raise ValueError(err)
 
-        outsize = N.prod(outshp)
-        insize = N.prod(inshp)
+        outsize = np.prod(outshp)
+        insize = np.prod(inshp)
 
         # range of output units over which to iterate
         if mode == 'valid':
-            lbound = N.array([kshp[0] - 1, kshp[1] - 1])
+            lbound = np.array([kshp[0] - 1, kshp[1] - 1])
             ubound = lbound + (inshp[1:] - kshp + 1)
         else:
-            lbound = N.zeros(2)
+            lbound = np.zeros(2)
             ubound = fulloutshp
 
         # coordinates of image in "fulloutshp" coordinates
-        topleft = N.array([kshp[0] - 1, kshp[1] - 1])
+        topleft = np.array([kshp[0] - 1, kshp[1] - 1])
         # bound when counting the receptive field
         botright = topleft + inshp[1:]
 
         # sparse matrix specifics...
         if ws:
-            spmatshp = (outsize * N.prod(kshp) * inshp[0], insize)
+            spmatshp = (outsize * np.prod(kshp) * inshp[0], insize)
         else:
             spmatshp = (nkern * outsize, insize)
         spmat = scipy_sparse.lil_matrix(spmatshp)
@@ -152,17 +151,17 @@ class ConvolutionIndices(Op):
 
                 # FOR EACH OUTPUT PIXEL...
                 # loop over output image height
-                for oy in N.arange(lbound[0], ubound[0], dy):
+                for oy in np.arange(lbound[0], ubound[0], dy):
                      # loop over output image width
-                    for ox in N.arange(lbound[1], ubound[1], dx):
+                    for ox in np.arange(lbound[1], ubound[1], dx):
 
                        # kern[l] is filter value to apply at (oj,oi)
                        # for (iy,ix)
                         l = 0
 
                         # ... ITERATE OVER INPUT UNITS IN RECEPTIVE FIELD
-                        for ky in oy + N.arange(kshp[0]):
-                            for kx in ox + N.arange(kshp[1]):
+                        for ky in oy + np.arange(kshp[0]):
+                            for kx in ox + np.arange(kshp[1]):
 
                                 # verify if we are still within image
                                 # boundaries. Equivalent to
@@ -173,13 +172,13 @@ class ConvolutionIndices(Op):
                                     # convert to "valid" input space
                                     # coords used to determine column
                                     # index to write to in sparse mat
-                                    iy, ix = N.array((ky, kx)) - topleft
+                                    iy, ix = np.array((ky, kx)) - topleft
                                     # determine raster-index of input pixel...
 
                                     # taking into account multiple
                                     # input features
                                     col = iy * inshp[2] + ix + \
-                                          fmapi * N.prod(inshp[1:])
+                                          fmapi * np.prod(inshp[1:])
 
                                     # convert oy,ox values to output
                                     # space coordinates
@@ -188,7 +187,7 @@ class ConvolutionIndices(Op):
                                     else:
                                         (y, x) = (oy, ox) - topleft
                                     # taking into account step size
-                                    (y, x) = N.array([y, x]) / (dy, dx)
+                                    (y, x) = np.array([y, x]) / (dy, dx)
 
                                     # convert to row index of sparse matrix
                                     if ws:
@@ -228,7 +227,7 @@ class ConvolutionIndices(Op):
         if ws:
             kmap = None
         else:
-            kmap = N.zeros(ntaps, dtype='int')
+            kmap = np.zeros(ntaps, dtype='int')
             k = 0
             # print 'TEMPORARY BUGFIX: REMOVE !!!'
             for j in xrange(spmat.shape[1]):
@@ -259,7 +258,7 @@ class ConvolutionIndices(Op):
         indices, indptr, spmatshp, outshp = self.evaluate(inshp, kshp)
         out_indices[0] = indices
         out_indptr[0] = indptr
-        spmat_shape[0] = numpy.asarray(spmatshp)
+        spmat_shape[0] = np.asarray(spmatshp)
 
 convolution_indices = ConvolutionIndices()
 
@@ -318,13 +317,12 @@ def convolve(kerns, kshp, nkern, images, imgshp, step=(1, 1), bias=None,
 
     :TODO: test for 1D and think of how to do n-d convolutions
     """
-    N = numpy
     # start by computing output dimensions, size, etc
-    kern_size = N.int64(N.prod(kshp))
+    kern_size = np.int64(np.prod(kshp))
 
     # inshp contains either 2 entries (height,width) or 3 (nfeatures,h,w)
     # in the first case, default nfeatures to 1
-    if N.size(imgshp) == 2:
+    if np.size(imgshp) == 2:
         imgshp = (1,) + imgshp
 
     # construct indices and index pointers for sparse matrix, which,
@@ -334,12 +332,12 @@ def convolve(kerns, kshp, nkern, images, imgshp, step=(1, 1), bias=None,
             convolution_indices.conv_eval(imgshp, kshp, step, mode)
 
     # build sparse matrix, then generate stack of image patches
-    csc = theano.sparse.CSM(sptype)(N.ones(indices.size), indices,
+    csc = theano.sparse.CSM(sptype)(np.ones(indices.size), indices,
                                     indptr, spmat_shape)
     patches = (sparse.structured_dot(csc, images.T)).T
 
     # compute output of linear classifier
-    pshape = tensor.stack([images.shape[0] * tensor.as_tensor(N.prod(outshp)),\
+    pshape = tensor.stack([images.shape[0] * tensor.as_tensor(np.prod(outshp)),\
                            tensor.as_tensor(imgshp[0] * kern_size)])
     patch_stack = tensor.reshape(patches, pshape, ndim=2)
 
@@ -354,14 +352,14 @@ def convolve(kerns, kshp, nkern, images, imgshp, step=(1, 1), bias=None,
     # now to have feature maps in raster order ...
     # go from bsize*outshp x nkern to bsize x nkern*outshp
     newshp = tensor.stack([images.shape[0],\
-                           tensor.as_tensor(N.prod(outshp)),\
+                           tensor.as_tensor(np.prod(outshp)),\
                            tensor.as_tensor(nkern)])
     tensout = tensor.reshape(output, newshp, ndim=3)
     output = tensor.DimShuffle((False,) * tensout.ndim, (0, 2, 1))(tensout)
     if flatten:
         output = tensor.flatten(output, 2)
 
-    return output, N.hstack((nkern, outshp))
+    return output, np.hstack((nkern, outshp))
 
 
 def max_pool(images, imgshp, maxpoolshp):
@@ -380,12 +378,11 @@ def max_pool(images, imgshp, maxpoolshp):
     :return: out1, symbolic result (2D tensor)
     :return: out2, logical shape of the output
     """
-    N = numpy
-    poolsize = N.int64(N.prod(maxpoolshp))
+    poolsize = np.int64(np.prod(maxpoolshp))
 
     # imgshp contains either 2 entries (height,width) or 3 (nfeatures,h,w)
     # in the first case, default nfeatures to 1
-    if N.size(imgshp) == 2:
+    if np.size(imgshp) == 2:
         imgshp = (1,) + imgshp
 
     # construct indices and index pointers for sparse matrix, which,
@@ -401,12 +398,12 @@ def max_pool(images, imgshp, maxpoolshp):
 #    print 'outshp = ', outshp
 
     # build sparse matrix, then generate stack of image patches
-    csc = theano.sparse.CSM(sptype)(N.ones(indices.size), indices,
+    csc = theano.sparse.CSM(sptype)(np.ones(indices.size), indices,
                                     indptr, spmat_shape)
     patches = sparse.structured_dot(csc, images.T).T
 
     pshape = tensor.stack([images.shape[0] *\
-                               tensor.as_tensor(N.prod(outshp)),
+                               tensor.as_tensor(np.prod(outshp)),
                            tensor.as_tensor(imgshp[0]),
                            tensor.as_tensor(poolsize)])
     patch_stack = tensor.reshape(patches, pshape, ndim=3)
@@ -414,7 +411,7 @@ def max_pool(images, imgshp, maxpoolshp):
     out1 = tensor.max(patch_stack, axis=2)
 
     pshape = tensor.stack([images.shape[0],
-                           tensor.as_tensor(N.prod(outshp)),
+                           tensor.as_tensor(np.prod(outshp)),
                            tensor.as_tensor(imgshp[0])])
     out2 = tensor.reshape(out1, pshape, ndim=3)
 

theano/sparse/sandbox/sp2.py

 from __future__ import absolute_import, print_function, division
-import numpy
+import numpy as np
 from six.moves import xrange
 import theano
 import scipy.sparse
@@ -74,7 +74,7 @@ class Poisson(gof.op.Op):
         assert _is_sparse(x)
         assert x.format in ["csr", "csc"]
         out[0] = x.copy()
-        out[0].data = numpy.asarray(numpy.random.poisson(out[0].data),
+        out[0].data = np.asarray(np.random.poisson(out[0].data),
                                     dtype=x.dtype)
         out[0].eliminate_zeros()
 
@@ -123,7 +123,7 @@ class Binomial(gof.op.Op):
     def perform(self, node, inputs, outputs):
         (n, p, shape) = inputs
         (out,) = outputs
-        binomial = numpy.random.binomial(n, p, size=shape)
+        binomial = np.random.binomial(n, p, size=shape)
         csx_matrix = getattr(scipy.sparse, self.format + '_matrix')
         out[0] = csx_matrix(binomial, dtype=self.dtype)
 
@@ -195,14 +195,14 @@ class Multinomial(gof.op.Op):
         if n.ndim == 0:
             for i in xrange(p.shape[0]):
                 k, l = p.indptr[i], p.indptr[i + 1]
-                out[0].data[k:l] = numpy.random.multinomial(n, p.data[k:l])
+                out[0].data[k:l] = np.random.multinomial(n, p.data[k:l])
         elif n.ndim == 1:
             if n.shape[0] != p.shape[0]:
                 raise ValueError('The number of element of n must be '
                                  'the same as the number of row of p.')
             for i in xrange(p.shape[0]):
                 k, l = p.indptr[i], p.indptr[i + 1]
-                out[0].data[k:l] = numpy.random.multinomial(n[i], p.data[k:l])
+                out[0].data[k:l] = np.random.multinomial(n[i], p.data[k:l])
 
     def grad(self, inputs, outputs_gradients):
         comment_n = "No gradient exists for the number of samples in class\

theano/sparse/sandbox/test_sp.py

@@ -11,7 +11,7 @@ if not theano.sparse.enable_sparse:
 import scipy.sparse
 from scipy.signal import convolve2d
 import scipy.sparse as sparse
-import numpy
+import numpy as np
 from six.moves import xrange
 
 from theano import function, tensor
@@ -43,8 +43,8 @@ class TestSP(unittest.TestCase):
         bias = tensor.dvector()
         kerns = tensor.dmatrix()
         input = tensor.dmatrix()
-        rng = numpy.random.RandomState(3423489)
-        filters = rng.randn(nkern, numpy.prod(kshp))
+        rng = np.random.RandomState(3423489)
+        filters = rng.randn(nkern, np.prod(kshp))
         biasvals = rng.randn(nkern)
 
         for mode in ('FAST_COMPILE', 'FAST_RUN'):
@@ -57,12 +57,12 @@ class TestSP(unittest.TestCase):
                     f = function([kerns, bias, input], output, mode=mode)
 
                     # now test with real values
-                    img2d = numpy.arange(bsize * numpy.prod(imshp)).reshape(( \
+                    img2d = np.arange(bsize * np.prod(imshp)).reshape(( \
                                                             bsize,) + imshp)
                     img1d = img2d.reshape(bsize, -1)
 
                     # create filters (need to be flipped to use convolve2d)
-                    filtersflipped = numpy.zeros((nkern,) + kshp)
+                    filtersflipped = np.zeros((nkern,) + kshp)
                     for k in range(nkern):
                         it = reversed(filters[k, :])
                         for i in range(kshp[0]):
@@ -71,11 +71,11 @@ class TestSP(unittest.TestCase):
 
                     # compute output with convolve2d
                     if conv_mode == 'valid':
-                        fulloutshp = numpy.array(imshp) - numpy.array(kshp) + 1
+                        fulloutshp = np.array(imshp) - np.array(kshp) + 1
                     else:
-                        fulloutshp = numpy.array(imshp) + numpy.array(kshp) - 1
+                        fulloutshp = np.array(imshp) + np.array(kshp) - 1
                     ntime1 = time.time()
-                    refout = numpy.zeros((bsize,)+tuple(fulloutshp)+(nkern,))
+                    refout = np.zeros((bsize,)+tuple(fulloutshp)+(nkern,))
                     for b in range(bsize):
                         for n in range(nkern):
                             refout[b, ..., n] = convolve2d(img2d[b, :, :],
@@ -88,7 +88,7 @@ class TestSP(unittest.TestCase):
                     bench1 += biasvals.reshape(1, 1, nkern)
 
                     # swap the last two dimensions (output needs to be nkern x outshp)
-                    bench1 = numpy.swapaxes(bench1, 1, 2)
+                    bench1 = np.swapaxes(bench1, 1, 2)
                     ttime1 = time.time()
                     out1 = f(filters, biasvals, img1d)
                     ttot += time.time() - ttime1
@@ -101,13 +101,13 @@ class TestSP(unittest.TestCase):
                     #downprop = function([kerns,input], vis, mode=mode)
                     #visval = downprop(filters,img1d)
                     # test downward propagation -- reference implementation
-                    #pshape = (img1d.shape[0],numpy.prod(outshp[1:]),numpy.prod(kshp))
-                    #patchstack = numpy.zeros(pshape)
-                    # for bi in numpy.arange(pshape[0]): # batch index
+                    #pshape = (img1d.shape[0],np.prod(outshp[1:]),np.prod(kshp))
+                    #patchstack = np.zeros(pshape)
+                    # for bi in np.arange(pshape[0]): # batch index
                         #abspos = 0
-                        # for outy in numpy.arange(outshp[1]):
-                            # for outx in numpy.arange(outshp[2]):
-                                # for ni in numpy.arange(nkern):
+                        # for outy in np.arange(outshp[1]):
+                            # for outx in np.arange(outshp[2]):
+                                # for ni in np.arange(nkern):
                                     # print 'filters[n,:].shape = ', filters[n,:].shape
                                     # print 'out1[bi,abspos].shape =',out1[bi,abspos].shape
                                     #patchstack[bi,abspos,:] = filters[n,:]*out1[bi,abspos]
@@ -115,13 +115,13 @@ class TestSP(unittest.TestCase):
                     #patchstack = patchstack.reshape(1,-1)
                     # indices, indptr, spmat_shape, sptype, outshp = \
                             # sp.convolution_indices.conv_eval(imshp,kshp,ss,conv_mode)
-                    #spmat = sparse.csc_matrix((numpy.ones_like(indices),indices,indptr),spmat_shape)
-                    #visref = numpy.dot(patchstack, spmat.todense())
+                    #spmat = sparse.csc_matrix((np.ones_like(indices),indices,indptr),spmat_shape)
+                    #visref = np.dot(patchstack, spmat.todense())
 
                     # print 'visval = ', visval
                     # print 'visref = ', visref
 
-                    #assert numpy.all(visref==visval)
+                    #assert np.all(visref==visval)
 
 
 #            print '**** Convolution Profiling Results (',mode,') ****'
@@ -143,10 +143,10 @@ class TestSP(unittest.TestCase):
         # symbolic stuff
         kerns = [tensor.dmatrix(), tensor.dmatrix()]
         input = tensor.dmatrix()
-        rng = numpy.random.RandomState(3423489)
+        rng = np.random.RandomState(3423489)
 
         # build actual input images
-        img2d = numpy.arange(bsize*numpy.prod(imshp)).reshape((bsize,)+imshp)
+        img2d = np.arange(bsize*np.prod(imshp)).reshape((bsize,)+imshp)
         img1d = img2d.reshape(bsize, -1)
 
         for mode in ('FAST_COMPILE', 'FAST_RUN'):
@@ -157,8 +157,8 @@ class TestSP(unittest.TestCase):
                             nkerns[0], input, imshp, ss[0], mode=conv_mode)
                     l1propup = function([kerns[0], input], l1hid, mode=mode)
 
-                    #l1kernvals = numpy.random.rand(nkerns[0],numpy.prod(kshp[0]))
-                    l1kernvals = numpy.arange(nkerns[0]*numpy.prod(kshp[0])).reshape(nkerns[0], numpy.prod(kshp[0]))
+                    #l1kernvals = np.random.rand(nkerns[0],np.prod(kshp[0]))
+                    l1kernvals = np.arange(nkerns[0]*np.prod(kshp[0])).reshape(nkerns[0], np.prod(kshp[0]))
                     l1hidval = l1propup(l1kernvals, img1d)
 
                     # actual values
@@ -166,17 +166,17 @@ class TestSP(unittest.TestCase):
                             nkerns[1], l1hid, l1shp, ss[1], mode=conv_mode)
                     l2propup = function([kerns[1], l1hid], l2hid, mode=mode)
 
-                    #l2kernvals = numpy.random.rand(nkerns[1],numpy.prod(kshp[1])*nkerns[0])
-                    l2kernvals = numpy.arange(nkerns[1]*numpy.prod(kshp[1])*nkerns[0]).reshape(nkerns[1], numpy.prod(kshp[1])*nkerns[0])
+                    #l2kernvals = np.random.rand(nkerns[1],np.prod(kshp[1])*nkerns[0])
+                    l2kernvals = np.arange(nkerns[1]*np.prod(kshp[1])*nkerns[0]).reshape(nkerns[1], np.prod(kshp[1])*nkerns[0])
                     # for debugging, we bring things back to integers
-                    l1hidval = numpy.arange(numpy.size(l1hidval)).reshape(l1hidval.shape)
+                    l1hidval = np.arange(np.size(l1hidval)).reshape(l1hidval.shape)
 
                     l2hidval = l2propup(l2kernvals, l1hidval)
 
     def test_maxpool(self):
         # generate flatted images
         maxpoolshps = ((2, 2), (3, 3), (4, 4), (5, 5), (6, 6))
-        imval = numpy.random.rand(4, 5, 10, 10)
+        imval = np.random.rand(4, 5, 10, 10)
 
         images = tensor.dmatrix()
         for maxpoolshp in maxpoolshps:
@@ -187,10 +187,10 @@ class TestSP(unittest.TestCase):
             output_val = f(imval.reshape(imval.shape[0], -1))
 
             # numeric verification
-            my_output_val = numpy.zeros((imval.shape[0], imval.shape[1],
+            my_output_val = np.zeros((imval.shape[0], imval.shape[1],
                                      imval.shape[2] // maxpoolshp[0],
                                      imval.shape[3] // maxpoolshp[1]))
-            assert numpy.prod(my_output_val.shape[1:]) == numpy.prod(numpy.r_[imval.shape[1], outshp])
+            assert np.prod(my_output_val.shape[1:]) == np.prod(np.r_[imval.shape[1], outshp])
 
             for n in range(imval.shape[0]):
                 for k in range(imval.shape[1]):
@@ -198,9 +198,9 @@ class TestSP(unittest.TestCase):
                         for j in range(imval.shape[3] // maxpoolshp[1]):
                             ii, jj = i*maxpoolshp[0], j*maxpoolshp[1]
                             patch = imval[n, k, ii:ii+maxpoolshp[0], jj:jj+maxpoolshp[1]]
-                            my_output_val[n, k, i, j] = numpy.max(patch)
+                            my_output_val[n, k, i, j] = np.max(patch)
             my_output_val = my_output_val.reshape(imval.shape[0], -1)
-            assert numpy.all(output_val == my_output_val)
+            assert np.all(output_val == my_output_val)
 
             def mp(input):
                 output, outshp = sp.max_pool(input, imval.shape[1:], maxpoolshp)

theano/sparse/sandbox/truedot.py

@@ -2,7 +2,7 @@ from __future__ import absolute_import, print_function, division
 import unittest
 
 import theano
-import numpy
+import numpy as np
 import scipy.sparse as sp
 
 from theano import sparse