removed unwanted files.

theano/compile/tests/test_debugprint.py

-import theano
-import theano.tensor as T
-
-def test_debugprint():
-    k = T.iscalar("k")
-    A = T.vector("A")
-
-    # Symbolic description of the result
-    result, updates = theano.scan(fn=lambda prior_result, A: prior_result * A,
-                                  outputs_info=T.ones_like(A),
-                                  non_sequences=A,
-                                  n_steps=k,
-                                  name="scan")
-
-    final_result = result[-1]
-
-    # compiled function that returns A**k
-    power = theano.function(inputs=[A, k],
-                            outputs=final_result,
-                            updates=updates,
-                            mode='DebugMode')
-
-    #a = theano.printing.debugprint(power, file="str")
-    #a = theano.compile.debugmode.debugprint(power,
-    #                                        prefix="test")
-
-    #print(a)
-    theano.printing.debugprint(power)
-    print power(range(10), 2)
-    print power(range(10), 4)
-
-test_debugprint()

theano/sandbox/cuda/check.py

-import theano; import numpy; a = theano.shared(numpy.zeros(100000).astype('float32'))

theano/sandbox/cuda/test_gpuarray.py

-from theano.sandbox.cuda.cula import gpu_solve
-import numpy as np
-import theano.tensor as TT
-import theano
-
-def thrash():
-    import numpy as np
-
-    A_val = np.asarray([[2, 0, 0], [0, 1, 0], [0, 0, 1]], dtype="float32")
-
-    #b_val = np.asarray([[0.5, 0, 0], [0, 0.5, 0], [0, 0, 0.5]], dtype="float32")
-    b_val = np.asarray([[0.5], [0.5], [0.5]], dtype="float32")
-
-    A_empty = np.zeros((3, 3)).astype("float32")
-    b_empty = np.zeros((3, 1)).astype("float32")
-
-    import theano
-    A = TT.matrix("A", dtype="float32")
-    b = TT.matrix("b", dtype="float32")
-
-    #theano.config.compute_test_value = 'warn'
-    #A.tag.test_value = A_val
-    #b.tag.test_value = b_val
-
-    #A = theano.shared(A_val)
-    #b = theano.shared(b_val)
-    from theano.misc.pycuda_utils import to_gpuarray
-
-    solver = gpu_solve(A, b)
-    fn = theano.function([A, b], [solver])
-    res = fn(A_val, b_val)
-    print(np.asarray(res[0]))
-    #import ipdb; ipdb.set_trace()
-
-def thrash2():
-    import numpy as np
-
-    A_val = np.asarray([[2, 0, 0], [0, 1, 0], [0, 0, 1]], dtype="float32")
-    #A_val = np.random.uniform(-0.01, 0.01, (10, 10)).astype("float32")
-    #A_val +=1
-    #A_val = np.linalg.svd(A_val)[0]
-    #A_val = (A_val + A_val.T) / 2.0
-
-    x_val = np.random.uniform(-0.4, 0.4, (A_val.shape[1], 1)).astype("float32")
-    b_val = np.dot(A_val, x_val)
-
-    #b_val = np.asarray([[0.5, 0, 0], [0, 0.5, 0], [0, 0, 0.5]], dtype="float32")
-    #b_val = np.asarray([[0.5], [0.5], [0.5]], dtype="float32")
-
-    #A_empty = np.zeros((A_val.shape[1], A_val.shape[1])).astype("float32")
-    x_res = np.zeros((A_val.shape[1], 1)).astype("float32")
-
-    import theano
-    A = TT.matrix("A", dtype="float32")
-    b = TT.matrix("b", dtype="float32")
-
-    #theano.config.compute_test_value = 'warn'
-    #A.tag.test_value = A_val
-    #b.tag.test_value = b_val
-
-    #A = theano.shared(A_val)
-    #b = theano.shared(b_val)
-
-    from theano.misc.pycuda_utils import to_gpuarray
-
-    solver = gpu_solve(A, b)
-    fn = theano.function([A, b], [solver])
-    res = fn(A_val, b_val)
-    res[0].get(x_res)
-    print(np.allclose(x_res, x_val))
-
-    import ipdb; ipdb.set_trace()
-
-
-thrash2()

theano/tests/test_debugprint.py

-import theano
-import theano.tensor as T
-from theano import printing
-
-
-X = T.matrix('X')
-results, updates = theano.scan(
-    fn=lambda x: 2*x.sum() + 1,
-    outputs_info=None,
-    sequences=[X],
-    non_sequences=None
-)
-
-printing.debugprint(theano.function([X], results))
-

theano/tests/test_scanmode.py

-import theano
-import theano.tensor as T
-from theano import printing
-
-
-X = T.matrix('X')
-results, updates = theano.scan(
-    fn=lambda x: 2*x.sum() + 3,
-    outputs_info=None,
-    sequences=[X],
-    non_sequences=None
-)
-printing.debugprint(results)