Python numpy.intp() 使用实例

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Example 1

def reset_index(self):
        """Reset index to range based
        """
        dfs = self.to_delayed()
        sizes = np.asarray(compute(*map(delayed(len), dfs)))
        prefixes = np.zeros_like(sizes)
        prefixes[1:] = np.cumsum(sizes[:-1])

        @delayed
        def fix_index(df, startpos):
            return df.set_index(np.arange(start=startpos,
                                          stop=startpos + len(df),
                                          dtype=np.intp))

        outdfs = [fix_index(df, startpos)
                  for df, startpos in zip(dfs, prefixes)]
        return from_delayed(outdfs) 

Example 2

def __init__(self, data, bucket_size=128):
        if bucket_size < 1:
            raise ValueError("A minimum bucket size of 1 is expected.")

        self._data = data
        self._n, self._k = self._data.shape
        self._nodes = None
        self._buckets = []
        self._bucket_size = bucket_size

        self._node_dtype = numpy.dtype([
            ('size', numpy.intp),
            ('bucket', numpy.intp),
            ('lower_bounds', (numpy.float_, self._k)),
            ('upper_bounds', (numpy.float_, self._k)),
        ])
        self._neighbour_dtype = numpy.dtype([
            ('squared_distance', numpy.float_),
            ('index', numpy.intp),
        ])

        self._build() 

Example 3

def test_reverse_strides_and_subspace_bufferinit(self):
        # This tests that the strides are not reversed for simple and
        # subspace fancy indexing.
        a = np.ones(5)
        b = np.zeros(5, dtype=np.intp)[::-1]
        c = np.arange(5)[::-1]

        a[b] = c
        # If the strides are not reversed, the 0 in the arange comes last.
        assert_equal(a[0], 0)

        # This also tests that the subspace buffer is initialized:
        a = np.ones((5, 2))
        c = np.arange(10).reshape(5, 2)[::-1]
        a[b, :] = c
        assert_equal(a[0], [0, 1]) 

Example 4

def test_unaligned(self):
        v = (np.zeros(64, dtype=np.int8) + ord('a'))[1:-7]
        d = v.view(np.dtype("S8"))
        # unaligned source
        x = (np.zeros(16, dtype=np.int8) + ord('a'))[1:-7]
        x = x.view(np.dtype("S8"))
        x[...] = np.array("b" * 8, dtype="S")
        b = np.arange(d.size)
        #trivial
        assert_equal(d[b], d)
        d[b] = x
        # nontrivial
        # unaligned index array
        b = np.zeros(d.size + 1).view(np.int8)[1:-(np.intp(0).itemsize - 1)]
        b = b.view(np.intp)[:d.size]
        b[...] = np.arange(d.size)
        assert_equal(d[b.astype(np.int16)], d)
        d[b.astype(np.int16)] = x
        # boolean
        d[b % 2 == 0]
        d[b % 2 == 0] = x[::2] 

Example 5

def test_count_func(self):
        # Tests count
        assert_equal(1, count(1))
        assert_equal(0, array(1, mask=[1]))

        ott = array([0., 1., 2., 3.], mask=[1, 0, 0, 0])
        res = count(ott)
        self.assertTrue(res.dtype.type is np.intp)
        assert_equal(3, res)

        ott = ott.reshape((2, 2))
        res = count(ott)
        assert_(res.dtype.type is np.intp)
        assert_equal(3, res)
        res = count(ott, 0)
        assert_(isinstance(res, ndarray))
        assert_equal([1, 2], res)
        assert_(getmask(res) is nomask)

        ott = array([0., 1., 2., 3.])
        res = count(ott, 0)
        assert_(isinstance(res, ndarray))
        assert_(res.dtype.type is np.intp)

        assert_raises(IndexError, ott.count, 1) 

Example 6

def check_function(self, function, sz):
        from threading import Thread

        out1 = np.empty((len(self.seeds),) + sz)
        out2 = np.empty((len(self.seeds),) + sz)

        # threaded generation
        t = [Thread(target=function, args=(np.random.RandomState(s), o))
             for s, o in zip(self.seeds, out1)]
        [x.start() for x in t]
        [x.join() for x in t]

        # the same serial
        for s, o in zip(self.seeds, out2):
            function(np.random.RandomState(s), o)

        # these platforms change x87 fpu precision mode in threads
        if (np.intp().dtype.itemsize == 4 and sys.platform == "win32"):
            np.testing.assert_array_almost_equal(out1, out2)
        else:
            np.testing.assert_array_equal(out1, out2) 

Example 7

def csc_matvec(mat_csc, vec, dense_output=True, dtype=None):
    v_nnz = vec.indices
    v_val = vec.data

    m_val = mat_csc.data
    m_ind = mat_csc.indices
    m_ptr = mat_csc.indptr

    res_dtype = dtype or np.result_type(mat_csc.dtype, vec.dtype)
    if dense_output:
        res = np.zeros((mat_csc.shape[0],), dtype=res_dtype)
        matvec2dense(m_ptr, m_ind, m_val, v_nnz, v_val, res)
    else:
        sizes = m_ptr.take(v_nnz+1) - m_ptr.take(v_nnz)
        sizes = np.concatenate(([0], np.cumsum(sizes)))
        n = sizes[-1]
        data = np.empty((n,), dtype=res_dtype)
        indices = np.empty((n,), dtype=np.intp)
        indptr = np.array([0, n], dtype=np.intp)
        matvec2sparse(m_ptr, m_ind, m_val, v_nnz, v_val, sizes, indices, data)
        res = sp.sparse.csr_matrix((data, indices, indptr), shape=(1, mat_csc.shape[0]), dtype=res_dtype)
        res.sum_duplicates() # expensive operation
    return res 

Example 8

def to_coo(self, tensor_mode=False):
        userid, itemid, feedback = self.fields
        user_item_data = self.training[[userid, itemid]].values

        if tensor_mode:
            # TODO this recomputes feedback data every new functon call,
            # but if data has not changed - no need for this, make a property
            new_feedback, feedback_transform = self.reindex(self.training, feedback, inplace=False)
            self.index = self.index._replace(feedback=feedback_transform)

            idx = np.hstack((user_item_data, new_feedback[:, np.newaxis]))
            idx = np.ascontiguousarray(idx)
            val = np.ones(self.training.shape[0],)
        else:
            idx = user_item_data
            val = self.training[feedback].values

        shp = tuple(idx.max(axis=0) + 1)
        idx = idx.astype(np.intp)
        val = np.ascontiguousarray(val)
        return idx, val, shp 

Example 9

def test_to_coo(self, tensor_mode=False):
        userid, itemid, feedback = self.fields
        test_data = self.test.testset

        user_idx = test_data[userid].values.astype(np.intp)
        item_idx = test_data[itemid].values.astype(np.intp)
        fdbk_val = test_data[feedback].values

        if tensor_mode:
            fdbk_idx = self.index.feedback.set_index('old').loc[fdbk_val, 'new'].values
            if np.isnan(fdbk_idx).any():
                raise NotImplementedError('Not all values of feedback are present in training data')
            else:
                fdbk_idx = fdbk_idx.astype(np.intp)
            test_coo = (user_idx, item_idx, fdbk_idx)
        else:
            test_coo = (user_idx, item_idx, fdbk_val)

        return test_coo 

Example 10

def _compile_and_prepare_functions(self, **kwargs):

        module_text = _module_reader(find_kernel('lomb'), self._cpp_defs)

        self.module = SourceModule(module_text, options=self.module_options)
        self.dtypes = dict(
            lomb=[np.intp, np.intp, np.intp, np.intp, np.int32,
                  self.real_type, self.real_type, np.int32, np.int32],
            lomb_dirsum=[np.intp, np.intp, np.intp, np.intp, np.intp,
                         np.int32, np.int32, self.real_type, self.real_type,
                         self.real_type, self.real_type, np.int32]
        )

        self.nfft_proc._compile_and_prepare_functions(**kwargs)
        for fname, dtype in self.dtypes.items():
            func = self.module.get_function(fname)
            self.prepared_functions[fname] = func.prepare(dtype)
        self.function_tuple = tuple(self.prepared_functions[fname]
                                    for fname in sorted(self.dtypes.keys())) 

Example 11

def default(self, obj):
        # convert dates and numpy objects in a json serializable format
        if isinstance(obj, datetime):
            return obj.strftime('%Y-%m-%dT%H:%M:%SZ')
        elif isinstance(obj, date):
            return obj.strftime('%Y-%m-%d')
        elif type(obj) in (np.int_, np.intc, np.intp, np.int8, np.int16,
                           np.int32, np.int64, np.uint8, np.uint16,
                           np.uint32, np.uint64):
            return int(obj)
        elif type(obj) in (np.bool_,):
            return bool(obj)
        elif type(obj) in (np.float_, np.float16, np.float32, np.float64,
                           np.complex_, np.complex64, np.complex128):
            return float(obj)

        # Let the base class default method raise the TypeError
        return json.JSONEncoder.default(self, obj) 

Example 12

def test_reverse_strides_and_subspace_bufferinit(self):
        # This tests that the strides are not reversed for simple and
        # subspace fancy indexing.
        a = np.ones(5)
        b = np.zeros(5, dtype=np.intp)[::-1]
        c = np.arange(5)[::-1]

        a[b] = c
        # If the strides are not reversed, the 0 in the arange comes last.
        assert_equal(a[0], 0)

        # This also tests that the subspace buffer is initialized:
        a = np.ones((5, 2))
        c = np.arange(10).reshape(5, 2)[::-1]
        a[b, :] = c
        assert_equal(a[0], [0, 1]) 

Example 13

def test_unaligned(self):
        v = (np.zeros(64, dtype=np.int8) + ord('a'))[1:-7]
        d = v.view(np.dtype("S8"))
        # unaligned source
        x = (np.zeros(16, dtype=np.int8) + ord('a'))[1:-7]
        x = x.view(np.dtype("S8"))
        x[...] = np.array("b" * 8, dtype="S")
        b = np.arange(d.size)
        #trivial
        assert_equal(d[b], d)
        d[b] = x
        # nontrivial
        # unaligned index array
        b = np.zeros(d.size + 1).view(np.int8)[1:-(np.intp(0).itemsize - 1)]
        b = b.view(np.intp)[:d.size]
        b[...] = np.arange(d.size)
        assert_equal(d[b.astype(np.int16)], d)
        d[b.astype(np.int16)] = x
        # boolean
        d[b % 2 == 0]
        d[b % 2 == 0] = x[::2] 

Example 14

def test_count_func(self):
        # Tests count
        assert_equal(1, count(1))
        assert_equal(0, array(1, mask=[1]))

        ott = array([0., 1., 2., 3.], mask=[1, 0, 0, 0])
        res = count(ott)
        self.assertTrue(res.dtype.type is np.intp)
        assert_equal(3, res)

        ott = ott.reshape((2, 2))
        res = count(ott)
        assert_(res.dtype.type is np.intp)
        assert_equal(3, res)
        res = count(ott, 0)
        assert_(isinstance(res, ndarray))
        assert_equal([1, 2], res)
        assert_(getmask(res) is nomask)

        ott = array([0., 1., 2., 3.])
        res = count(ott, 0)
        assert_(isinstance(res, ndarray))
        assert_(res.dtype.type is np.intp)

        assert_raises(IndexError, ott.count, 1) 

Example 15

def check_function(self, function, sz):
        from threading import Thread

        out1 = np.empty((len(self.seeds),) + sz)
        out2 = np.empty((len(self.seeds),) + sz)

        # threaded generation
        t = [Thread(target=function, args=(np.random.RandomState(s), o))
             for s, o in zip(self.seeds, out1)]
        [x.start() for x in t]
        [x.join() for x in t]

        # the same serial
        for s, o in zip(self.seeds, out2):
            function(np.random.RandomState(s), o)

        # these platforms change x87 fpu precision mode in threads
        if (np.intp().dtype.itemsize == 4 and sys.platform == "win32"):
            np.testing.assert_array_almost_equal(out1, out2)
        else:
            np.testing.assert_array_equal(out1, out2) 

Example 16

def nn_vec_basic(arr1, arr2, topn, sort=True, return_sims=False, nthreads=8):
    """
    For each row in arr1 (m1 x d) find topn most similar rows from arr2 (m2 x d). Similarity is defined as dot product.
    Please note, that in the case of normalized rows in arr1 and arr2 dot product will be equal to cosine and will be
    monotonically decreasing function of Eualidean distance.
    :param arr1: array of vectors to find nearest neighbours for
    :param arr2: array of vectors to search for nearest neighbours in
    :param topn: number of nearest neighbours
    :param sort: indices in i-th row of returned array should sort corresponding rows of arr2 in descending order of
    similarity to i-th row of arr2
    :param return_sims: return similarities along with indices of nearest neighbours
    :param nthreads:
    :return: array (m1 x topn) where i-th row contains indices of rows in arr2 most similar to i-th row of m1, and, if
    return_sims=True, an array (m1 x topn) of corresponding similarities.
    """
    sims = np.dot(arr1, arr2.T)
    best_inds = argmaxk_rows(sims, topn, sort=sort, nthreads=nthreads)
    if not return_sims:
        return best_inds

    # generate row indices corresponding to best_inds (just current row id in each row) (m x k)
    rows = np.arange(best_inds.shape[0], dtype=np.intp)[:, np.newaxis].repeat(best_inds.shape[1], axis=1)
    return best_inds, sims[rows, best_inds] 

Example 17

def argmaxk_rows_opt1(arr, k=10, sort=False):
    """
    Optimized implementation. When sort=False it is equal to argmaxk_rows_basic. When sort=True and k << arr.shape[1],
    it is should be faster, because we argsort only subarray of k max elements from each row of arr (arr.shape[0] x k) instead of
    the whole array arr (arr.shape[0] x arr.shape[1]).
    """
    best_inds = np.argpartition(arr, kth=-k, axis=1)[:, -k:]  # column indices of k max elements in each row (m x k)
    if not sort:
        return best_inds
    # generate row indices corresponding to best_ids (just current row id in each row) (m x k)
    rows = np.arange(best_inds.shape[0], dtype=np.intp)[:, np.newaxis].repeat(best_inds.shape[1], axis=1)
    best_elems = arr[rows, best_inds]  # select k max elements from each row using advanced indexing (m x k)
    # indices which sort each row of best_elems in descending order (m x k)
    best_elems_inds = np.argsort(best_elems, axis=1)[:, ::-1]
    # reorder best_indices so that arr[i, sorted_best_inds[i,:]] will be sorted in descending order
    sorted_best_inds = best_inds[rows, best_elems_inds]
    return sorted_best_inds 

Example 18

def test_reverse_strides_and_subspace_bufferinit(self):
        # This tests that the strides are not reversed for simple and
        # subspace fancy indexing.
        a = np.ones(5)
        b = np.zeros(5, dtype=np.intp)[::-1]
        c = np.arange(5)[::-1]

        a[b] = c
        # If the strides are not reversed, the 0 in the arange comes last.
        assert_equal(a[0], 0)

        # This also tests that the subspace buffer is initialized:
        a = np.ones((5, 2))
        c = np.arange(10).reshape(5, 2)[::-1]
        a[b, :] = c
        assert_equal(a[0], [0, 1]) 

Example 19

def test_unaligned(self):
        v = (np.zeros(64, dtype=np.int8) + ord('a'))[1:-7]
        d = v.view(np.dtype("S8"))
        # unaligned source
        x = (np.zeros(16, dtype=np.int8) + ord('a'))[1:-7]
        x = x.view(np.dtype("S8"))
        x[...] = np.array("b" * 8, dtype="S")
        b = np.arange(d.size)
        #trivial
        assert_equal(d[b], d)
        d[b] = x
        # nontrivial
        # unaligned index array
        b = np.zeros(d.size + 1).view(np.int8)[1:-(np.intp(0).itemsize - 1)]
        b = b.view(np.intp)[:d.size]
        b[...] = np.arange(d.size)
        assert_equal(d[b.astype(np.int16)], d)
        d[b.astype(np.int16)] = x
        # boolean
        d[b % 2 == 0]
        d[b % 2 == 0] = x[::2] 

Example 20

def test_count_func(self):
        # Tests count
        assert_equal(1, count(1))
        assert_equal(0, array(1, mask=[1]))

        ott = array([0., 1., 2., 3.], mask=[1, 0, 0, 0])
        res = count(ott)
        self.assertTrue(res.dtype.type is np.intp)
        assert_equal(3, res)

        ott = ott.reshape((2, 2))
        res = count(ott)
        assert_(res.dtype.type is np.intp)
        assert_equal(3, res)
        res = count(ott, 0)
        assert_(isinstance(res, ndarray))
        assert_equal([1, 2], res)
        assert_(getmask(res) is nomask)

        ott = array([0., 1., 2., 3.])
        res = count(ott, 0)
        assert_(isinstance(res, ndarray))
        assert_(res.dtype.type is np.intp)

        assert_raises(IndexError, ott.count, 1) 

Example 21

def check_function(self, function, sz):
        from threading import Thread

        out1 = np.empty((len(self.seeds),) + sz)
        out2 = np.empty((len(self.seeds),) + sz)

        # threaded generation
        t = [Thread(target=function, args=(np.random.RandomState(s), o))
             for s, o in zip(self.seeds, out1)]
        [x.start() for x in t]
        [x.join() for x in t]

        # the same serial
        for s, o in zip(self.seeds, out2):
            function(np.random.RandomState(s), o)

        # these platforms change x87 fpu precision mode in threads
        if (np.intp().dtype.itemsize == 4 and sys.platform == "win32"):
            np.testing.assert_array_almost_equal(out1, out2)
        else:
            np.testing.assert_array_equal(out1, out2) 

Example 22

def test_reverse_strides_and_subspace_bufferinit(self):
        # This tests that the strides are not reversed for simple and
        # subspace fancy indexing.
        a = np.ones(5)
        b = np.zeros(5, dtype=np.intp)[::-1]
        c = np.arange(5)[::-1]

        a[b] = c
        # If the strides are not reversed, the 0 in the arange comes last.
        assert_equal(a[0], 0)

        # This also tests that the subspace buffer is initialized:
        a = np.ones((5, 2))
        c = np.arange(10).reshape(5, 2)[::-1]
        a[b, :] = c
        assert_equal(a[0], [0, 1]) 

Example 23

def test_unaligned(self):
        v = (np.zeros(64, dtype=np.int8) + ord('a'))[1:-7]
        d = v.view(np.dtype("S8"))
        # unaligned source
        x = (np.zeros(16, dtype=np.int8) + ord('a'))[1:-7]
        x = x.view(np.dtype("S8"))
        x[...] = np.array("b" * 8, dtype="S")
        b = np.arange(d.size)
        #trivial
        assert_equal(d[b], d)
        d[b] = x
        # nontrivial
        # unaligned index array
        b = np.zeros(d.size + 1).view(np.int8)[1:-(np.intp(0).itemsize - 1)]
        b = b.view(np.intp)[:d.size]
        b[...] = np.arange(d.size)
        assert_equal(d[b.astype(np.int16)], d)
        d[b.astype(np.int16)] = x
        # boolean
        d[b % 2 == 0]
        d[b % 2 == 0] = x[::2] 

Example 24

def test_count_func(self):
        # Tests count
        assert_equal(1, count(1))
        assert_equal(0, array(1, mask=[1]))

        ott = array([0., 1., 2., 3.], mask=[1, 0, 0, 0])
        res = count(ott)
        self.assertTrue(res.dtype.type is np.intp)
        assert_equal(3, res)

        ott = ott.reshape((2, 2))
        res = count(ott)
        assert_(res.dtype.type is np.intp)
        assert_equal(3, res)
        res = count(ott, 0)
        assert_(isinstance(res, ndarray))
        assert_equal([1, 2], res)
        assert_(getmask(res) is nomask)

        ott = array([0., 1., 2., 3.])
        res = count(ott, 0)
        assert_(isinstance(res, ndarray))
        assert_(res.dtype.type is np.intp)

        assert_raises(IndexError, ott.count, 1) 

Example 25

def check_function(self, function, sz):
        from threading import Thread

        out1 = np.empty((len(self.seeds),) + sz)
        out2 = np.empty((len(self.seeds),) + sz)

        # threaded generation
        t = [Thread(target=function, args=(np.random.RandomState(s), o))
             for s, o in zip(self.seeds, out1)]
        [x.start() for x in t]
        [x.join() for x in t]

        # the same serial
        for s, o in zip(self.seeds, out2):
            function(np.random.RandomState(s), o)

        # these platforms change x87 fpu precision mode in threads
        if (np.intp().dtype.itemsize == 4 and sys.platform == "win32"):
            np.testing.assert_array_almost_equal(out1, out2)
        else:
            np.testing.assert_array_equal(out1, out2) 

Example 26

def check_function(self, function, sz):
        from threading import Thread

        out1 = np.empty((len(self.seeds),) + sz)
        out2 = np.empty((len(self.seeds),) + sz)

        # threaded generation
        t = [Thread(target=function, args=(mt19937.RandomState(s), o))
             for s, o in zip(self.seeds, out1)]
        [x.start() for x in t]
        [x.join() for x in t]

        # the same serial
        for s, o in zip(self.seeds, out2):
            function(mt19937.RandomState(s), o)

        # these platforms change x87 fpu precision mode in threads
        if np.intp().dtype.itemsize == 4 and sys.platform == "win32":
            assert_array_almost_equal(out1, out2)
        else:
            assert_array_equal(out1, out2) 

Example 27

def test_reverse_strides_and_subspace_bufferinit(self):
        # This tests that the strides are not reversed for simple and
        # subspace fancy indexing.
        a = np.ones(5)
        b = np.zeros(5, dtype=np.intp)[::-1]
        c = np.arange(5)[::-1]

        a[b] = c
        # If the strides are not reversed, the 0 in the arange comes last.
        assert_equal(a[0], 0)

        # This also tests that the subspace buffer is initialized:
        a = np.ones((5, 2))
        c = np.arange(10).reshape(5, 2)[::-1]
        a[b, :] = c
        assert_equal(a[0], [0, 1]) 

Example 28

def test_unaligned(self):
        v = (np.zeros(64, dtype=np.int8) + ord('a'))[1:-7]
        d = v.view(np.dtype("S8"))
        # unaligned source
        x = (np.zeros(16, dtype=np.int8) + ord('a'))[1:-7]
        x = x.view(np.dtype("S8"))
        x[...] = np.array("b" * 8, dtype="S")
        b = np.arange(d.size)
        #trivial
        assert_equal(d[b], d)
        d[b] = x
        # nontrivial
        # unaligned index array
        b = np.zeros(d.size + 1).view(np.int8)[1:-(np.intp(0).itemsize - 1)]
        b = b.view(np.intp)[:d.size]
        b[...] = np.arange(d.size)
        assert_equal(d[b.astype(np.int16)], d)
        d[b.astype(np.int16)] = x
        # boolean
        d[b % 2 == 0]
        d[b % 2 == 0] = x[::2] 

Example 29

def test_count_func(self):
        # Tests count
        assert_equal(1, count(1))
        assert_equal(0, array(1, mask=[1]))

        ott = array([0., 1., 2., 3.], mask=[1, 0, 0, 0])
        res = count(ott)
        self.assertTrue(res.dtype.type is np.intp)
        assert_equal(3, res)

        ott = ott.reshape((2, 2))
        res = count(ott)
        assert_(res.dtype.type is np.intp)
        assert_equal(3, res)
        res = count(ott, 0)
        assert_(isinstance(res, ndarray))
        assert_equal([1, 2], res)
        assert_(getmask(res) is nomask)

        ott = array([0., 1., 2., 3.])
        res = count(ott, 0)
        assert_(isinstance(res, ndarray))
        assert_(res.dtype.type is np.intp)
        assert_raises(ValueError, ott.count, axis=1) 

Example 30

def check_function(self, function, sz):
        from threading import Thread

        out1 = np.empty((len(self.seeds),) + sz)
        out2 = np.empty((len(self.seeds),) + sz)

        # threaded generation
        t = [Thread(target=function, args=(np.random.RandomState(s), o))
             for s, o in zip(self.seeds, out1)]
        [x.start() for x in t]
        [x.join() for x in t]

        # the same serial
        for s, o in zip(self.seeds, out2):
            function(np.random.RandomState(s), o)

        # these platforms change x87 fpu precision mode in threads
        if (np.intp().dtype.itemsize == 4 and sys.platform == "win32"):
            np.testing.assert_array_almost_equal(out1, out2)
        else:
            np.testing.assert_array_equal(out1, out2) 

Example 31

def default(self, obj):
        # convert dates and numpy objects in a json serializable format
        if isinstance(obj, datetime):
            return obj.strftime('%Y-%m-%dT%H:%M:%SZ')
        elif isinstance(obj, date):
            return obj.strftime('%Y-%m-%d')
        elif type(obj) in [np.int_, np.intc, np.intp, np.int8, np.int16,
                           np.int32, np.int64, np.uint8, np.uint16,
                           np.uint32, np.uint64]:
            return int(obj)
        elif type(obj) in [np.bool_]:
            return bool(obj)
        elif type(obj) in [np.float_, np.float16, np.float32, np.float64,
                           np.complex_, np.complex64, np.complex128]:
            return float(obj)

        # Let the base class default method raise the TypeError
        return json.JSONEncoder.default(self, obj) 

Example 32

def test_reverse_strides_and_subspace_bufferinit(self):
        # This tests that the strides are not reversed for simple and
        # subspace fancy indexing.
        a = np.ones(5)
        b = np.zeros(5, dtype=np.intp)[::-1]
        c = np.arange(5)[::-1]

        a[b] = c
        # If the strides are not reversed, the 0 in the arange comes last.
        assert_equal(a[0], 0)

        # This also tests that the subspace buffer is initialized:
        a = np.ones((5, 2))
        c = np.arange(10).reshape(5, 2)[::-1]
        a[b, :] = c
        assert_equal(a[0], [0, 1]) 

Example 33

def test_unaligned(self):
        v = (np.zeros(64, dtype=np.int8) + ord('a'))[1:-7]
        d = v.view(np.dtype("S8"))
        # unaligned source
        x = (np.zeros(16, dtype=np.int8) + ord('a'))[1:-7]
        x = x.view(np.dtype("S8"))
        x[...] = np.array("b" * 8, dtype="S")
        b = np.arange(d.size)
        #trivial
        assert_equal(d[b], d)
        d[b] = x
        # nontrivial
        # unaligned index array
        b = np.zeros(d.size + 1).view(np.int8)[1:-(np.intp(0).itemsize - 1)]
        b = b.view(np.intp)[:d.size]
        b[...] = np.arange(d.size)
        assert_equal(d[b.astype(np.int16)], d)
        d[b.astype(np.int16)] = x
        # boolean
        d[b % 2 == 0]
        d[b % 2 == 0] = x[::2] 

Example 34

def test_big_indices(self):
        # ravel_multi_index for big indices (issue #7546)
        if np.intp == np.int64:
            arr = ([1, 29], [3, 5], [3, 117], [19, 2],
                   [2379, 1284], [2, 2], [0, 1])
            assert_equal(
                np.ravel_multi_index(arr, (41, 7, 120, 36, 2706, 8, 6)),
                [5627771580, 117259570957])

        # test overflow checking for too big array (issue #7546)
        dummy_arr = ([0],[0])
        half_max = np.iinfo(np.intp).max // 2
        assert_equal(
            np.ravel_multi_index(dummy_arr, (half_max, 2)), [0])
        assert_raises(ValueError,
            np.ravel_multi_index, dummy_arr, (half_max+1, 2))
        assert_equal(
            np.ravel_multi_index(dummy_arr, (half_max, 2), order='F'), [0])
        assert_raises(ValueError,
            np.ravel_multi_index, dummy_arr, (half_max+1, 2), order='F') 

Example 35

def test_count_func(self):
        # Tests count
        assert_equal(1, count(1))
        assert_equal(0, array(1, mask=[1]))

        ott = array([0., 1., 2., 3.], mask=[1, 0, 0, 0])
        res = count(ott)
        self.assertTrue(res.dtype.type is np.intp)
        assert_equal(3, res)

        ott = ott.reshape((2, 2))
        res = count(ott)
        assert_(res.dtype.type is np.intp)
        assert_equal(3, res)
        res = count(ott, 0)
        assert_(isinstance(res, ndarray))
        assert_equal([1, 2], res)
        assert_(getmask(res) is nomask)

        ott = array([0., 1., 2., 3.])
        res = count(ott, 0)
        assert_(isinstance(res, ndarray))
        assert_(res.dtype.type is np.intp)
        assert_raises(ValueError, ott.count, axis=1) 

Example 36

def check_function(self, function, sz):
        from threading import Thread

        out1 = np.empty((len(self.seeds),) + sz)
        out2 = np.empty((len(self.seeds),) + sz)

        # threaded generation
        t = [Thread(target=function, args=(np.random.RandomState(s), o))
             for s, o in zip(self.seeds, out1)]
        [x.start() for x in t]
        [x.join() for x in t]

        # the same serial
        for s, o in zip(self.seeds, out2):
            function(np.random.RandomState(s), o)

        # these platforms change x87 fpu precision mode in threads
        if np.intp().dtype.itemsize == 4 and sys.platform == "win32":
            assert_array_almost_equal(out1, out2)
        else:
            assert_array_equal(out1, out2) 

Example 37

def predict(self, X):
        if not hasattr(self, "classes_"):        
            raise ValueError('fit')
            
        if self.normalize_:
            X = self._sc_X.fit_transform(X)
            
        X_ = self.transform(X)
        y_pred = self.estimator.predict(X_)
        return   self.classes_.take(np.asarray(y_pred, dtype=np.intp))

#        elif self.predict_with == 'all':
#
#            predict_ = []
#            
#            for mask in self.mask_:
#                self.estimator.fit(X=self.transform(self.X_, mask=mask), y=self.y_)
#                X_ = self.transform(X, mask=mask)
#                y_pred = self.estimator.predict(X_)
#                predict_.append(self.classes_.take(np.asarray(y_pred, dtype=np.intp)))
#            return np.asarray(predict_) 

Example 38

def predict(self, X):
        if not hasattr(self, "classes_"):        
            raise ValueError('fit')
            
        if self.normalize_:
            X = self._sc_X.fit_transform(X)
            
        X_ = self.transform(X)
        y_pred = self.estimator.predict(X_)
        return   self.classes_.take(np.asarray(y_pred, dtype=np.intp))

#        elif self.predict_with == 'all':
#
#            predict_ = []
#            
#            for mask in self.mask_:
#                self.estimator.fit(X=self.transform(self.X_, mask=mask), y=self.y_)
#                X_ = self.transform(X, mask=mask)
#                y_pred = self.estimator.predict(X_)
#                predict_.append(self.classes_.take(np.asarray(y_pred, dtype=np.intp)))
#            return np.asarray(predict_) 

Example 39

def get_curand_int_func():
    code = """
#include "curand_kernel.h"
extern "C" {
__global__ void 
rand_setup(curandStateXORWOW_t* state, int size, unsigned long long seed)
{
    int tid = threadIdx.x + blockIdx.x * blockDim.x;
    int total_threads = blockDim.x * gridDim.x;

    for(int i = tid; i < size; i+=total_threads)
    {
        curand_init(seed, i, 0, &state[i]);
    }
}
}
    """
    mod = SourceModule(code, no_extern_c = True)
    func = mod.get_function("rand_setup")
    func.prepare('PiL')#[np.intp, np.int32, np.uint64])
    return func 

Example 40

def get_fill_function(dtype, pitch = True):
    type_dst = dtype_to_ctype(dtype)
    name = "fill"
    
    if pitch:
        func = SourceModule(
            fill_pitch_template % {
                    "name": name,
                    "type_dst": type_dst
            }, options=["--ptxas-options=-v"]).get_function(name)
        func.prepare('iiPi'+np.dtype(dtype).char)
        #    [np.int32, np.int32, np.intp, np.int32, _get_type(dtype)])
    else:
        func = SourceModule(
                fill_nonpitch_template % {
                    "name": name,
                    "type_dst": type_dst
                },
                options=["--ptxas-options=-v"]).get_function(name)
        func.prepare('iP'+np.dtype(dtype).char)#[np.int32, np.intp, _get_type(dtype)])
    return func 

Example 41

def get_transpose_function(dtype, conj = False):
    src_type = dtype_to_ctype(dtype)
    name = "trans"
    operation = ""
    
    if conj:
        if dtype == np.complex128:
            operation = "pycuda::conj"
        elif dtype == np.complex64:
            operation = "pycuda::conj"
    
    func = SourceModule(
            transpose_template % {
                "name": name,
                "type": src_type,
                "operation": operation
            },
            options=["--ptxas-options=-v"]).get_function(name)
    func.prepare('iiPiPi')#[np.int32, np.int32, np.intp,
    #              np.int32, np.intp, np.int32])
    return func 

Example 42

def npy2py_type(npy_type):
    int_types = [
        np.int_, np.intc, np.intp, np.int8, np.int16, np.int32, np.int64,
        np.uint8, np.uint16, np.uint32, np.uint64
    ]

    float_types = [np.float_, np.float16, np.float32, np.float64]

    bytes_types = [np.str_, np.string_]

    if npy_type in int_types:
        return int
    if npy_type in float_types:
        return float
    if npy_type in bytes_types:
        return bytes

    if hasattr(npy_type, 'char'):
        if npy_type.char in ['S', 'a']:
            return bytes
        raise TypeError

    return npy_type 

Example 43

def test_multinomial_binary():
    # Test multinomial LR on a binary problem.
    target = (iris.target > 0).astype(np.intp)
    target = np.array(["setosa", "not-setosa"])[target]

    for solver in ['lbfgs', 'newton-cg', 'sag']:
        clf = LogisticRegression(solver=solver, multi_class='multinomial',
                                 random_state=42, max_iter=2000)
        clf.fit(iris.data, target)

        assert_equal(clf.coef_.shape, (1, iris.data.shape[1]))
        assert_equal(clf.intercept_.shape, (1,))
        assert_array_equal(clf.predict(iris.data), target)

        mlr = LogisticRegression(solver=solver, multi_class='multinomial',
                                 random_state=42, fit_intercept=False)
        mlr.fit(iris.data, target)
        pred = clf.classes_[np.argmax(clf.predict_log_proba(iris.data),
                                      axis=1)]
        assert_greater(np.mean(pred == target), .9) 

Example 44

def test_int_float_dict():
    rng = np.random.RandomState(0)
    keys = np.unique(rng.randint(100, size=10).astype(np.intp))
    values = rng.rand(len(keys))

    d = IntFloatDict(keys, values)
    for key, value in zip(keys, values):
        assert_equal(d[key], value)
    assert_equal(len(d), len(keys))

    d.append(120, 3.)
    assert_equal(d[120], 3.0)
    assert_equal(len(d), len(keys) + 1)
    for i in xrange(2000):
        d.append(i + 1000, 4.0)
    assert_equal(d[1100], 4.0) 

Example 45

def get_indices(self, i):
        """Row and column indices of the i'th bicluster.

        Only works if ``rows_`` and ``columns_`` attributes exist.

        Returns
        -------
        row_ind : np.array, dtype=np.intp
            Indices of rows in the dataset that belong to the bicluster.
        col_ind : np.array, dtype=np.intp
            Indices of columns in the dataset that belong to the bicluster.

        """
        rows = self.rows_[i]
        columns = self.columns_[i]
        return np.nonzero(rows)[0], np.nonzero(columns)[0] 

Example 46

def predict(self, X):
        """Perform classification on samples in X.

        For an one-class model, +1 or -1 is returned.

        Parameters
        ----------
        X : {array-like, sparse matrix}, shape (n_samples, n_features)
            For kernel="precomputed", the expected shape of X is
            [n_samples_test, n_samples_train]

        Returns
        -------
        y_pred : array, shape (n_samples,)
            Class labels for samples in X.
        """
        y = super(BaseSVC, self).predict(X)
        return self.classes_.take(np.asarray(y, dtype=np.intp))

    # Hacky way of getting predict_proba to raise an AttributeError when
    # probability=False using properties. Do not use this in new code; when
    # probabilities are not available depending on a setting, introduce two
    # estimators. 

Example 47

def test_reverse_strides_and_subspace_bufferinit(self):
        # This tests that the strides are not reversed for simple and
        # subspace fancy indexing.
        a = np.ones(5)
        b = np.zeros(5, dtype=np.intp)[::-1]
        c = np.arange(5)[::-1]

        a[b] = c
        # If the strides are not reversed, the 0 in the arange comes last.
        assert_equal(a[0], 0)

        # This also tests that the subspace buffer is initialized:
        a = np.ones((5, 2))
        c = np.arange(10).reshape(5, 2)[::-1]
        a[b, :] = c
        assert_equal(a[0], [0, 1]) 

Example 48

def test_unaligned(self):
        v = (np.zeros(64, dtype=np.int8) + ord('a'))[1:-7]
        d = v.view(np.dtype("S8"))
        # unaligned source
        x = (np.zeros(16, dtype=np.int8) + ord('a'))[1:-7]
        x = x.view(np.dtype("S8"))
        x[...] = np.array("b" * 8, dtype="S")
        b = np.arange(d.size)
        #trivial
        assert_equal(d[b], d)
        d[b] = x
        # nontrivial
        # unaligned index array
        b = np.zeros(d.size + 1).view(np.int8)[1:-(np.intp(0).itemsize - 1)]
        b = b.view(np.intp)[:d.size]
        b[...] = np.arange(d.size)
        assert_equal(d[b.astype(np.int16)], d)
        d[b.astype(np.int16)] = x
        # boolean
        d[b % 2 == 0]
        d[b % 2 == 0] = x[::2] 

Example 49

def fit(self, X, y, **kwargs):
        # Determine output settings
        n_samples, self.n_features_ = X.shape
        if self.max_features is None:
            self.max_features = 'auto'

        y = np.atleast_1d(y)

        if y.ndim == 1:
            # reshape is necessary to preserve the data contiguity against vs
            # [:, np.newaxis] that does not.
            y = np.reshape(y, (-1, 1))

        self.n_outputs_ = y.shape[1]
        self.classes_ = [None] * self.n_outputs_
        self.n_classes_ = [1] * self.n_outputs_
        self.n_classes_ = np.array(self.n_classes_, dtype=np.intp)

        if getattr(y, "dtype", None) != DOUBLE or not y.flags.contiguous:
            y = np.ascontiguousarray(y, dtype=DOUBLE)

        if len(y) != n_samples:
            raise ValueError(
                "Number of labels=%d does not match number of samples=%d"
                % (len(y), n_samples))

        # Build tree
        self.tree_ = ExtraTree(
            self.max_features, self.min_samples_split, self.n_classes_,
            self.n_outputs_, self.classification)
        self.tree_.build(X, y)

        if self.n_outputs_ == 1:
            self.n_classes_ = self.n_classes_[0]
            self.classes_ = self.classes_[0]

        return self 

Example 50

def test_intp(self,level=rlevel):
        # Ticket #99
        i_width = np.int_(0).nbytes*2 - 1
        np.intp('0x' + 'f'*i_width, 16)
        self.assertRaises(OverflowError, np.intp, '0x' + 'f'*(i_width+1), 16)
        self.assertRaises(ValueError, np.intp, '0x1', 32)
        assert_equal(255, np.intp('0xFF', 16))
        assert_equal(1024, np.intp(1024)) 
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