Python numpy.cumprod方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import cumprod [as 別名]
def __init__(self, embedded_op, numBasisEls, actionInds,
                 blocksizes, embedded_dim, nComponentsInActiveBlock,
                 iActiveBlock, nBlocks, dim):

        self.embedded = embedded_op
        self.numBasisEls = numBasisEls
        self.actionInds = actionInds
        self.blocksizes = blocksizes

        numBasisEls_noop_blankaction = numBasisEls.copy()
        for i in actionInds: numBasisEls_noop_blankaction[i] = 1
        self.basisInds_noop_blankaction = [list(range(n)) for n in numBasisEls_noop_blankaction]

        # multipliers to go from per-label indices to tensor-product-block index
        # e.g. if map(len,basisInds) == [1,4,4] then multipliers == [ 16 4 1 ]
        self.multipliers = _np.array(_np.flipud(_np.cumprod([1] + list(
            reversed(list(numBasisEls[1:]))))), _np.int64)
        self.basisInds_action = [list(range(numBasisEls[i])) for i in actionInds]

        self.embeddedDim = embedded_dim
        self.nComponents = nComponentsInActiveBlock
        self.iActiveBlock = iActiveBlock
        self.nBlocks = nBlocks
        self.offset = sum(blocksizes[0:iActiveBlock])
        super(DMOpRep_Embedded, self).__init__(dim) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import cumprod [as 別名]
def test_basic(self):
        ba = [1, 2, 10, 11, 6, 5, 4]
        ba2 = [[1, 2, 3, 4], [5, 6, 7, 9], [10, 3, 4, 5]]
        for ctype in [np.int16, np.uint16, np.int32, np.uint32,
                      np.float32, np.float64, np.complex64, np.complex128]:
            a = np.array(ba, ctype)
            a2 = np.array(ba2, ctype)
            if ctype in ['1', 'b']:
                assert_raises(ArithmeticError, np.cumprod, a)
                assert_raises(ArithmeticError, np.cumprod, a2, 1)
                assert_raises(ArithmeticError, np.cumprod, a)
            else:
                assert_array_equal(np.cumprod(a, axis=-1),
                                   np.array([1, 2, 20, 220,
                                             1320, 6600, 26400], ctype))
                assert_array_equal(np.cumprod(a2, axis=0),
                                   np.array([[1, 2, 3, 4],
                                             [5, 12, 21, 36],
                                             [50, 36, 84, 180]], ctype))
                assert_array_equal(np.cumprod(a2, axis=-1),
                                   np.array([[1, 2, 6, 24],
                                             [5, 30, 210, 1890],
                                             [10, 30, 120, 600]], ctype)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import cumprod [as 別名]
def _discount_reward_tensor_1d(reward, sequence_length,
                               discount=1., dtype=None):
    if sequence_length is None:
        raise ValueError('sequence_length must not be `None` for 1D reward.')

    batch_size = tf.shape(reward)[0]
    max_seq_length = tf.reduce_max(sequence_length)
    dtype = dtype or reward.dtype

    if discount == 1.:
        dmat = tf.ones(
            tf.concat([[batch_size], [max_seq_length]], 0), dtype=dtype)
    else:
        mask = tf.sequence_mask(sequence_length, dtype=dtype)
        mask = tf.concat([mask[:, 1:], tf.zeros_like(mask[:, -1:])], axis=1)
        # Make each row = [discount, ..., discount, 1, ..., 1]
        dmat = mask * discount + (1 - mask)
        dmat = tf.cumprod(dmat, axis=1, reverse=True)

    disc_reward = dmat * tf.expand_dims(reward, -1)
    disc_reward = mask_sequences(
        disc_reward, sequence_length, dtype=dtype, tensor_rank=2)

    return disc_reward 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import cumprod [as 別名]
def _call_impl(self, t):
        x = (t - self.t_old) / self.h
        if t.ndim == 0:
            p = np.tile(x, self.order + 1)
            p = np.cumprod(p)
        else:
            p = np.tile(x, (self.order + 1, 1))
            p = np.cumprod(p, axis=0)
        # Here we don't multiply by h, not a mistake.
        y = np.dot(self.Q, p)
        if y.ndim == 2:
            y += self.y_old[:, None]
        else:
            y += self.y_old

        return y 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import cumprod [as 別名]
def test_basic(self):
        ba = [1, 2, 10, 11, 6, 5, 4]
        ba2 = [[1, 2, 3, 4], [5, 6, 7, 9], [10, 3, 4, 5]]
        for ctype in [np.int16, np.uint16, np.int32, np.uint32,
                      np.float32, np.float64, np.complex64, np.complex128]:
            a = np.array(ba, ctype)
            a2 = np.array(ba2, ctype)
            if ctype in ['1', 'b']:
                self.assertRaises(ArithmeticError, np.cumprod, a)
                self.assertRaises(ArithmeticError, np.cumprod, a2, 1)
                self.assertRaises(ArithmeticError, np.cumprod, a)
            else:
                assert_array_equal(np.cumprod(a, axis=-1),
                                   np.array([1, 2, 20, 220,
                                             1320, 6600, 26400], ctype))
                assert_array_equal(np.cumprod(a2, axis=0),
                                   np.array([[1, 2, 3, 4],
                                             [5, 12, 21, 36],
                                             [50, 36, 84, 180]], ctype))
                assert_array_equal(np.cumprod(a2, axis=-1),
                                   np.array([[1, 2, 6, 24],
                                             [5, 30, 210, 1890],
                                             [10, 30, 120, 600]], ctype)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import cumprod [as 別名]
def cumprod(x, axis=None):
    """Return the cumulative product of the elements along a given axis.

    Wraping of numpy.cumprod.

    Parameters
    ----------
    x
        Input tensor variable.

    axis
        The axis along which the cumulative product is computed.
        The default (None) is to compute the cumprod over the flattened array.

    .. versionadded:: 0.7

    """
    return CumprodOp(axis=axis)(x) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import cumprod [as 別名]
def reshape_workaround(data, shape_out):  # type: (TensorOp, Sequence[int]) -> TensorOp
    """Limited workaround for tensor reshape operation."""
    shape_in = data.shape.lengths

    if np.prod(shape_in) != np.prod(shape_out):
        raise ValueError('Total size of input (%d) and output (%d) dimension mismatch.',
                         np.prod(shape_in), np.prod(shape_out))

    ndims_out = len(shape_out)
    if ndims_out == 1:
        tensor = ng.flatten(data)
    elif ndims_out == 2:
        cumprods = list(np.cumprod(shape_in))
        flatten_at_idx = cumprods.index(shape_out[0]) + 1
        tensor = ng.flatten_at(data, flatten_at_idx)
    else:
        raise NotImplementedError('Reshape can only support flatten to 1d or 2d.')

    return ng.cast_axes(tensor, make_pos_axes(shape_out)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import cumprod [as 別名]
def test_basic(self):
        ba = [1, 2, 10, 11, 6, 5, 4]
        ba2 = [[1, 2, 3, 4], [5, 6, 7, 9], [10, 3, 4, 5]]
        for ctype in [np.int16, np.uint16, np.int32, np.uint32,
                np.float32, np.float64, np.complex64, np.complex128]:
            a = np.array(ba, ctype)
            a2 = np.array(ba2, ctype)
            if ctype in ['1', 'b']:
                self.assertRaises(ArithmeticError, cumprod, a)
                self.assertRaises(ArithmeticError, cumprod, a2, 1)
                self.assertRaises(ArithmeticError, cumprod, a)
            else:
                assert_array_equal(np.cumprod(a, axis= -1),
                        np.array([1, 2, 20, 220, 1320, 6600, 26400], ctype))
                assert_array_equal(np.cumprod(a2, axis=0),
                        np.array([[ 1, 2, 3, 4], [ 5, 12, 21, 36],
                            [50, 36, 84, 180]], ctype))
                assert_array_equal(np.cumprod(a2, axis= -1),
                        np.array([[ 1, 2, 6, 24], [ 5, 30, 210, 1890],
                            [10, 30, 120, 600]], ctype)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import cumprod [as 別名]
def sub2ind(siz, subs, **kwargs):
    """
    assumes column-order major
    """
    # subs is a list
    assert len(siz) == len(subs), \
        'found inconsistent siz and subs: %d %d' % (len(siz), len(subs))

    k = np.cumprod(siz[::-1])

    ndx = subs[-1]
    for i, v in enumerate(subs[:-1][::-1]):
        ndx = ndx + v * k[i]

    return ndx





###############################################################################
# functions from some external source
############################################################################### 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import cumprod [as 別名]
def encoded_1d(samples):
        """ Returns a unique label for each combination of samples """
        # from sklearn.preprocessing import MultiLabelBinarizer
        encoded_2d = samples.encoded_2d()
        class_space = [v.n_classes for k, v in samples.items()]
        offsets = np.array([1] + np.cumprod(class_space).tolist()[:-1])[None, :]
        encoded_1d = (offsets * encoded_2d).sum(axis=1)
        # e = MultiLabelBinarizer()
        # bin_coeff = e.fit_transform(encoded_2d)
        # bin_basis = (2 ** np.arange(bin_coeff.shape[1]))[None, :]
        # # encoded_1d = (bin_coeff * bin_basis).sum(axis=1)
        # encoded_1d = (bin_coeff * bin_basis[::-1]).sum(axis=1)
        # # vt.unique_rows(sklearn.preprocessing.MultiLabelBinarizer().fit_transform(encoded_2d))
        # [v.encoded_df.values for k, v in samples.items()]
        # encoded_df_1d = pd.concat([v.encoded_df for k, v in samples.items()], axis=1)
        return encoded_1d 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import cumprod [as 別名]
def testCumProdAndSum(self):

    def run_test(arr, *args, **kwargs):
      for fn in self.array_transforms:
        arg = fn(arr)
        self.match(
            array_ops.cumprod(arg, *args, **kwargs),
            np.cumprod(arg, *args, **kwargs))
        self.match(
            array_ops.cumsum(arg, *args, **kwargs),
            np.cumsum(arg, *args, **kwargs))

    run_test([])
    run_test([1, 2, 3])
    run_test([1, 2, 3], dtype=float)
    run_test([1, 2, 3], dtype=np.float32)
    run_test([1, 2, 3], dtype=np.float64)
    run_test([1., 2., 3.])
    run_test([1., 2., 3.], dtype=int)
    run_test([1., 2., 3.], dtype=np.int32)
    run_test([1., 2., 3.], dtype=np.int64)
    run_test([[1, 2], [3, 4]], axis=1)
    run_test([[1, 2], [3, 4]], axis=0)
    run_test([[1, 2], [3, 4]], axis=-1)
    run_test([[1, 2], [3, 4]], axis=-2) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import cumprod [as 別名]
def polar_to_cartesian(arr, r):
    a = np.concatenate((np.array([2 * np.pi]), arr))
    si = np.sin(a)
    si[0] = 1
    si = np.cumprod(si)
    co = np.cos(a)
    co = np.roll(co, -1)
    return si * co * r 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import cumprod [as 別名]
def test_nancumprod(self):
        tgt = np.cumprod(self.mat)
        for mat in self.integer_arrays():
            assert_equal(np.nancumprod(mat), tgt) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import cumprod [as 別名]
def test_allnans(self):
        for f, tgt_value in zip(self.nanfuncs, [0, 1]):
            # Unlike other nan-functions, sum/prod/cumsum/cumprod don't warn on all nan input
            with assert_no_warnings():
                res = f([np.nan]*3, axis=None)
                tgt = tgt_value*np.ones((3))
                assert_(np.array_equal(res, tgt), 'result is not %s * np.ones((3))' % (tgt_value))
                # Check scalar
                res = f(np.nan)
                tgt = tgt_value*np.ones((1))
                assert_(np.array_equal(res, tgt), 'result is not %s * np.ones((1))' % (tgt_value))
                # Check there is no warning for not all-nan
                f([0]*3, axis=None) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import cumprod [as 別名]
def test_result_values(self):
        for axis in (-2, -1, 0, 1, None):
            tgt = np.cumprod(_ndat_ones, axis=axis)
            res = np.nancumprod(_ndat, axis=axis)
            assert_almost_equal(res, tgt)
            tgt = np.cumsum(_ndat_zeros,axis=axis)
            res = np.nancumsum(_ndat, axis=axis)
            assert_almost_equal(res, tgt)