Python numpy.isscalar方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import isscalar [as 別名]
def handle_zeros_in_scale(scale, copy=True):
    ''' Makes sure that whenever scale is zero, we handle it correctly.
    This happens in most scalers when we have constant features.
    Adapted from sklearn.preprocessing.data'''

    # if we are fitting on 1D arrays, scale might be a scalar
    if np.isscalar(scale):
        if scale == .0:
            scale = 1.
        return scale
    elif isinstance(scale, np.ndarray):
        if copy:
            # New array to avoid side-effects
            scale = scale.copy()
        scale[scale == 0.0] = 1.0
    return scale 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import isscalar [as 別名]
def extract_params_as_shared_arrays(link):
    assert isinstance(link, chainer.Link)
    shared_arrays = {}
    for param_name, param in link.namedparams():
        typecode = param.array.dtype.char
        shared_arrays[param_name] = mp.RawArray(typecode, param.array.ravel())

    for persistent_name, persistent in chainerrl.misc.namedpersistent(link):
        if isinstance(persistent, np.ndarray):
            typecode = persistent.dtype.char
            shared_arrays[persistent_name] = mp.RawArray(
                typecode, persistent.ravel())
        else:
            assert np.isscalar(persistent)
            # Wrap by a 1-dim array because multiprocessing.RawArray does not
            # accept a 0-dim array.
            persistent_as_array = np.asarray([persistent])
            typecode = persistent_as_array.dtype.char
            shared_arrays[persistent_name] = mp.RawArray(
                typecode, persistent_as_array)
    return shared_arrays 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import isscalar [as 別名]
def compute_policy_gradient_full_correction(
        action_distrib, action_distrib_mu, action_value, v,
        truncation_threshold):
    """Compute off-policy bias correction term wrt all actions."""
    assert truncation_threshold is not None
    assert np.isscalar(v)
    with chainer.no_backprop_mode():
        rho_all_inv = compute_full_importance(action_distrib_mu,
                                              action_distrib)
        correction_weight = (
            np.maximum(1 - truncation_threshold * rho_all_inv,
                       np.zeros_like(rho_all_inv)) *
            action_distrib.all_prob.array[0])
        correction_advantage = action_value.q_values.array[0] - v
    return -F.sum(correction_weight *
                  action_distrib.all_log_prob *
                  correction_advantage, axis=1) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import isscalar [as 別名]
def compute_policy_gradient_sample_correction(
        action_distrib, action_distrib_mu, action_value, v,
        truncation_threshold):
    """Compute off-policy bias correction term wrt a sampled action."""
    assert np.isscalar(v)
    assert truncation_threshold is not None
    with chainer.no_backprop_mode():
        sample_action = action_distrib.sample().array
        rho_dash_inv = compute_importance(
            action_distrib_mu, action_distrib, sample_action)
        if (truncation_threshold > 0 and
                rho_dash_inv >= 1 / truncation_threshold):
            return chainer.Variable(np.asarray([0], dtype=np.float32))
        correction_weight = max(0, 1 - truncation_threshold * rho_dash_inv)
        assert correction_weight <= 1
        q = float(action_value.evaluate_actions(sample_action).array[0])
        correction_advantage = q - v
    return -(correction_weight *
             action_distrib.log_prob(sample_action) *
             correction_advantage) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import isscalar [as 別名]
def add_pixels(self, uv_px, img1d, weight=None):
        # Lookup row & column for each in-bounds coordinate.
        mask = self.get_mask(uv_px)
        xx = uv_px[0,mask]
        yy = uv_px[1,mask]
        # Update matrix according to assigned weight.
        if weight is None:
            img1d[mask] = self.img[yy,xx]
        elif np.isscalar(weight):
            img1d[mask] += self.img[yy,xx] * weight
        else:
            w1 = np.asmatrix(weight, dtype='float32')
            w3 = w1.transpose() * np.ones((1,3))
            img1d[mask] += np.multiply(self.img[yy,xx], w3[mask])


# A panorama image made from several FisheyeImage sources.
# TODO: Add support for supersampled anti-aliasing filters. 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import isscalar [as 別名]
def convert_dictionary_keys_to_dense_indices(dictionary):
    """Convert the keys to tuples containing integers.

    Example
    -------
    >>> dictionary = {(0.0, 1): 0, 2: 1}
    >>> convert_dictionary_keys_to_dense_indices(dictionary)
    {(0, 1): 0, (2,): 1}

    """
    new_dictionary = {}
    for key, val in dictionary.items():
        new_key = (int(key),) if np.isscalar(key) else tuple(int(i) for i in key)
        new_dictionary[new_key] = val

    return new_dictionary 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import isscalar [as 別名]
def sparse_to_dense(voxel_data, dims, dtype=np.bool):
    if voxel_data.ndim != 2 or voxel_data.shape[0] != 3:
        raise ValueError('voxel_data is wrong shape; should be 3xN array.')
    if np.isscalar(dims):
        dims = [dims] * 3
    dims = np.atleast_2d(dims).T
    # truncate to integers
    xyz = voxel_data.astype(np.int)
    # discard voxels that fall outside dims
    valid_ix = ~np.any((xyz < 0) | (xyz >= dims), 0)
    xyz = xyz[:, valid_ix]
    out = np.zeros(dims.flatten(), dtype=dtype)
    out[tuple(xyz)] = True
    return out

# def get_linear_index(x, y, z, dims):
# """ Assuming xzy order. (y increasing fastest.
# TODO ensure this is right when dims are not all same
# """
# return x*(dims[1]*dims[2]) + z*dims[1] + y 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import isscalar [as 別名]
def __init__(self, zval, pz, shape, var_axes=(0,),\
                 is_complex=False,name=None):
                                 
        # Convert scalars to arrays
        if np.isscalar(zval):
            zval = np.array([zval])
        if np.isscalar(pz):
            pz = np.array([pz])
            
        # Set parameters of base estimator
        dtype = zval.dtype
        BaseEst.__init__(self,shape=shape, var_axes=var_axes, dtype=dtype, name=name,\
            type_name='DiscreteEst', nvars=1, cost_avail=True)
                        
        # Set parameters
        self.zval = zval
        self.pz = pz
        self.shape = shape
        self.is_complex = is_complex
        self.fz = -np.log(pz) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import isscalar [as 別名]
def _tojson(*numpy_objs):
    '''Utility function which returns a list where each element of numpy_objs
    is converted to its python equivalent (float or list)'''
    ret = []
    # problem: browsers might not be happy with JSON 'NAN', so convert
    # NaNs to None. Unfortunately, the conversion must be done element wise
    # in numpy (seems not to exist a pandas na filter):
    for obj in numpy_objs:
        isscalar = np.isscalar(obj)
        nan_indices = None if isscalar else \
            np.argwhere(np.isnan(obj)).flatten()
        # note: numpy.float64(N).tolist() returns a python float, so:
        obj = None if isscalar and np.isnan(obj) else obj.tolist()
        if nan_indices is not None:
            for idx in nan_indices:
                obj[idx] = None
        ret.append(obj)

    return ret  # tuple(_.tolist() for _ in numpy_objs) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import isscalar [as 別名]
def gen_random_legcharge_nq(chinfo, ind_len, n_qsector):
    """return a random (unsorted) LegCharge with a given number of charge sectors.

    `nqsector` gives the (desired) number of sectors for each of the charges.
    """
    if np.isscalar(n_qsector):
        n_qsector = [n_qsector] * chinfo.qnumber
    n_qsector = np.asarray(n_qsector, dtype=np.intp)
    if n_qsector.shape != (chinfo.qnumber, ):
        raise ValueError
    slices = rand_partitions(0, ind_len, np.prod(n_qsector, dtype=int))
    qs = np.zeros((len(slices) - 1, len(n_qsector)), int)
    q_combos = [a for a in it.product(*[range(-(nq // 2), nq // 2 + 1) for nq in n_qsector])]
    qs = np.array(q_combos)[rand_distinct_int(0, len(q_combos) - 1, len(slices) - 1), :]
    qs = chinfo.make_valid(qs)
    return npc.LegCharge.from_qind(chinfo, slices, qs) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import isscalar [as 別名]
def gen_random_legcharge_nq(chinfo, ind_len, n_qsector):
    """return a random (unsorted) LegCharge with a given number of charge sectors.

    `nqsector` gives the (desired) number of sectors for each of the charges.
    """
    if np.isscalar(n_qsector):
        n_qsector = [n_qsector] * chinfo.qnumber
    n_qsector = np.asarray(n_qsector, dtype=np.intp)
    if n_qsector.shape != (chinfo.qnumber, ):
        raise ValueError
    slices = rand_partitions(0, ind_len, np.prod(n_qsector, dtype=int))
    qs = np.zeros((len(slices) - 1, len(n_qsector)), int)
    q_combos = [a for a in it.product(*[range(-(nq // 2), nq // 2 + 1) for nq in n_qsector])]
    qs = np.array(q_combos)[rand_distinct_int(0, len(q_combos) - 1, len(slices) - 1), :]
    qs = chinfo.make_valid(qs)
    return charges.LegCharge.from_qind(chinfo, slices, qs) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import isscalar [as 別名]
def test_dense_embeddings(make_categories, reps, layer):
    """Test the embedding layer."""
    x, K = make_categories
    x = np.repeat(x, reps, axis=-1)
    N = len(x)
    S = 3
    x_, X_ = _make_placeholders(x, S, tf.int32)
    output, reg = layer(output_dim=D, n_categories=K)(X_)

    tc = tf.test.TestCase()
    with tc.test_session():
        tf.global_variables_initializer().run()
        r = reg.eval()

        assert np.isscalar(r)
        assert r >= 0

        Phi = output.eval(feed_dict={x_: x})

        assert Phi.shape == (S, N, D * reps) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import isscalar [as 別名]
def test_dense_outputs(dense, make_data):
    """Make sure the dense layers output expected dimensions."""
    x, _, _ = make_data
    S = 3

    x_, X_ = _make_placeholders(x, S)
    N = x.shape[0]

    Phi, KL = dense(output_dim=D)(X_)

    tc = tf.test.TestCase()
    with tc.test_session():
        tf.global_variables_initializer().run()
        P = Phi.eval(feed_dict={x_: x})
        assert P.shape == (S, N, D)
        assert P.dtype == np.float32
        assert np.isscalar(KL.eval(feed_dict={x_: x})) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import isscalar [as 別名]
def test_kl_gaussian_normal(random):
    """Test Gaussian/Normal KL."""
    dim = (5, 10)
    Dim = (5, 10, 10)

    mu0 = random.randn(*dim).astype(np.float32)
    L0 = random_chol(Dim)
    q = tfp.distributions.MultivariateNormalTriL(mu0, L0)

    mu1 = random.randn(*dim).astype(np.float32)
    std1 = 1.0
    L1 = [(std1 * np.eye(dim[1])).astype(np.float32) for _ in range(dim[0])]
    p = tf.distributions.Normal(mu1, std1)

    KL = kl_sum(q, p)
    KLr = KLdiv(mu0, L0, mu1, L1)

    tc = tf.test.TestCase()
    with tc.test_session():
        kl = KL.eval()
        assert np.isscalar(kl)
        assert np.allclose(kl, KLr) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import isscalar [as 別名]
def __add__(self, other):
        if np.isscalar(other):
            other = Constant(other, "Constant({})".format(other))
            name = "Add({},{})".format(self.name, other.name)
            return BinOp(np.add, name)(self, other)
        assert isinstance(other, Node)
        name = "Add({},{})".format(self.name, other.name)
        return BinOp(np.add, name)(self, other)