Python numpy.place方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import place [as 別名]
def one_shot_method(prediction, x, curr_sample, curr_target, p_t):
    grad_est = np.zeros((BATCH_SIZE, IMAGE_ROWS, IMAGE_COLS, NUM_CHANNELS))
    DELTA = np.random.randint(2, size=(BATCH_SIZE, IMAGE_ROWS, IMAGE_COLS, NUM_CHANNELS))
    np.place(DELTA, DELTA==0, -1)

    y_plus = np.clip(curr_sample + args.delta * DELTA, CLIP_MIN, CLIP_MAX)
    y_minus = np.clip(curr_sample - args.delta * DELTA, CLIP_MIN, CLIP_MAX)

    if args.CW_loss == 0:
        pred_plus = K.get_session().run([prediction], feed_dict={x: y_plus, K.learning_phase(): 0})[0]
        pred_plus_t = pred_plus[np.arange(BATCH_SIZE), list(curr_target)]

        pred_minus = K.get_session().run([prediction], feed_dict={x: y_minus, K.learning_phase(): 0})[0]
        pred_minus_t = pred_minus[np.arange(BATCH_SIZE), list(curr_target)]

        num_est = (pred_plus_t - pred_minus_t)

    grad_est = num_est[:, None, None, None]/(args.delta * DELTA)

    # Getting gradient of the loss
    if args.CW_loss == 0:
        loss_grad = -1.0 * grad_est/p_t[:, None, None, None]

    return loss_grad 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import place [as 別名]
def denormalize(self, data):
        data_array, dict_flag = self.mode_checker(data)
        for name in data_array.keys():  # normalize data for each named inputs
            magic_mask = [data_array[name] == MAGIC_NUMBER]

            if self._custom_denorm_func is not None:
                data_array[name] = self._custom_denorm_func(data_array[name])
            data_array[name] *= self.std_labels[name]
            data_array[name] += self.mean_labels[name]

            np.place(data_array[name], magic_mask, MAGIC_NUMBER)

        if not dict_flag:
            data_array = data_array["Temp"]
            self.mean_labels = self.mean_labels['Temp']
            self.std_labels = self.std_labels['Temp']

        return data_array 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import place [as 別名]
def kurtosis(a, axis=0, fisher=True, bias=True):
    a, axis = _chk_asarray(a, axis)
    m2 = moment(a,2,axis)
    m4 = moment(a,4,axis)
    olderr = np.seterr(all='ignore')
    try:
        vals = ma.where(m2 == 0, 0, m4 / m2**2.0)
    finally:
        np.seterr(**olderr)

    if not bias:
        n = a.count(axis)
        can_correct = (n > 3) & (m2 is not ma.masked and m2 > 0)
        if can_correct.any():
            n = np.extract(can_correct, n)
            m2 = np.extract(can_correct, m2)
            m4 = np.extract(can_correct, m4)
            nval = 1.0/(n-2)/(n-3)*((n*n-1.0)*m4/m2**2.0-3*(n-1)**2.0)
            np.place(vals, can_correct, nval+3.0)
    if fisher:
        return vals - 3
    else:
        return vals 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import place [as 別名]
def _get_data(self):
        if any(not os.path.exists(path) or not check_sha1(path, sha1) for path, sha1 in
               ((os.path.join(self._root, name), sha1) for _, name, sha1 in self._train_data + self._test_data)):
            for url, _, sha1 in self._train_data + self._test_data:
                download(url=url, path=self._root, sha1_hash=sha1)

        if self._mode == "train":
            data_files = self._train_data[0]
        else:
            data_files = self._test_data[0]

        import scipy.io as sio

        loaded_mat = sio.loadmat(os.path.join(self._root, data_files[1]))

        data = loaded_mat["X"]
        data = np.transpose(data, (3, 0, 1, 2))
        self._data = mx.nd.array(data, dtype=data.dtype)

        self._label = loaded_mat["y"].astype(np.int32).squeeze()
        np.place(self._label, self._label == 10, 0) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import place [as 別名]
def _flatten_fdir(self, fdir, flat_idx, dirmap, copy=False):
        # WARNING: This modifies fdir in place if copy is set to False!
        if copy:
            fdir = fdir.copy()
        shape = fdir.shape
        go_to = (
             0 - shape[1],
             1 - shape[1],
             1 + 0,
             1 + shape[1],
             0 + shape[1],
            -1 + shape[1],
            -1 + 0,
            -1 - shape[1]
            )
        gotomap = dict(zip(dirmap, go_to))
        for k, v in gotomap.items():
            fdir[fdir == k] = v
        fdir.flat[flat_idx] += flat_idx 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import place [as 別名]
def set_nodata(self, data_name, new_nodata, old_nodata=None):
        """
        Change nodata value of a dataset.
 
        Parameters
        ----------
        data_name : string
                    Attribute name of dataset to change.
        new_nodata : int or float
                     New nodata value to use.
        old_nodata : int or float (optional)
                     If none provided, defaults to
                     self.<data_name>.<nodata>
        """
        if old_nodata is None:
            old_nodata = getattr(self, data_name).nodata
        data = getattr(self, data_name)
        if np.isnan(old_nodata):
            np.place(data, np.isnan(data), new_nodata)
        else:
            np.place(data, data == old_nodata, new_nodata)
        data.nodata = new_nodata 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import place [as 別名]
def _cdf(self, x, c):
        output = np.zeros(x.shape, dtype=x.dtype)
        val = (1.0+c)/(1.0-c)
        c1 = x < np.pi
        c2 = 1-c1
        xp = np.extract(c1, x)
        xn = np.extract(c2, x)
        if np.any(xn):
            valn = np.extract(c2, np.ones_like(x)*val)
            xn = 2*np.pi - xn
            yn = np.tan(xn/2.0)
            on = 1.0-1.0/np.pi*np.arctan(valn*yn)
            np.place(output, c2, on)
        if np.any(xp):
            valp = np.extract(c1, np.ones_like(x)*val)
            yp = np.tan(xp/2.0)
            op = 1.0/np.pi*np.arctan(valp*yp)
            np.place(output, c1, op)
        return output 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import place [as 別名]
def place(arr, mask, vals):
    """
    Change elements of an array based on conditional and input values.

    Similar to ``np.copyto(arr, vals, where=mask)``, the difference is that
    `place` uses the first N elements of `vals`, where N is the number of
    True values in `mask`, while `copyto` uses the elements where `mask`
    is True.

    Note that `extract` does the exact opposite of `place`.

    Parameters
    ----------
    arr : ndarray
        Array to put data into.
    mask : array_like
        Boolean mask array. Must have the same size as `a`.
    vals : 1-D sequence
        Values to put into `a`. Only the first N elements are used, where
        N is the number of True values in `mask`. If `vals` is smaller
        than N, it will be repeated, and if elements of `a` are to be masked,
        this sequence must be non-empty.

    See Also
    --------
    copyto, put, take, extract

    Examples
    --------
    >>> arr = np.arange(6).reshape(2, 3)
    >>> np.place(arr, arr>2, [44, 55])
    >>> arr
    array([[ 0,  1,  2],
           [44, 55, 44]])

    """
    if not isinstance(arr, np.ndarray):
        raise TypeError("argument 1 must be numpy.ndarray, "
                        "not {name}".format(name=type(arr).__name__))

    return _insert(arr, mask, vals) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import place [as 別名]
def _update_dim_sizes(dim_sizes, arg, core_dims):
    """
    Incrementally check and update core dimension sizes for a single argument.

    Arguments
    ---------
    dim_sizes : Dict[str, int]
        Sizes of existing core dimensions. Will be updated in-place.
    arg : ndarray
        Argument to examine.
    core_dims : Tuple[str, ...]
        Core dimensions for this argument.
    """
    if not core_dims:
        return

    num_core_dims = len(core_dims)
    if arg.ndim < num_core_dims:
        raise ValueError(
            '%d-dimensional argument does not have enough '
            'dimensions for all core dimensions %r'
            % (arg.ndim, core_dims))

    core_shape = arg.shape[-num_core_dims:]
    for dim, size in zip(core_dims, core_shape):
        if dim in dim_sizes:
            if size != dim_sizes[dim]:
                raise ValueError(
                    'inconsistent size for core dimension %r: %r vs %r'
                    % (dim, size, dim_sizes[dim]))
        else:
            dim_sizes[dim] = size 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import place [as 別名]
def substitute_values(self, vect):
        """
        Internal method to substitute integers into the vector, and construct
        metadata to convert back to the original vector.

        np.nan is always given -1, all other objects are given integers in
        order of apperence.

        Parameters
        ----------
        vect : np.array
            the vector in which to substitute values in
        """

        try:
            unique = np.unique(vect)
        except:
            unique = set(vect)

        unique = [
            x for x in unique if not isinstance(x, float) or not isnan(x)
        ]

        arr = np.copy(vect)
        for new_id, value in enumerate(unique):
            np.place(arr, arr==value, new_id)
            self.metadata[new_id] = value
        arr = arr.astype(np.float)
        np.place(arr, np.isnan(arr), -1)
        self.arr = arr

        if -1 in arr:
            self.metadata[-1] = self._missing_id 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import place [as 別名]
def add_newdoc(place, obj, doc):
    """
    Adds documentation to obj which is in module place.

    If doc is a string add it to obj as a docstring

    If doc is a tuple, then the first element is interpreted as
       an attribute of obj and the second as the docstring
          (method, docstring)

    If doc is a list, then each element of the list should be a
       sequence of length two --> [(method1, docstring1),
       (method2, docstring2), ...]

    This routine never raises an error.

    This routine cannot modify read-only docstrings, as appear
    in new-style classes or built-in functions. Because this
    routine never raises an error the caller must check manually
    that the docstrings were changed.
    """
    try:
        new = getattr(__import__(place, globals(), {}, [obj]), obj)
        if isinstance(doc, str):
            add_docstring(new, doc.strip())
        elif isinstance(doc, tuple):
            add_docstring(getattr(new, doc[0]), doc[1].strip())
        elif isinstance(doc, list):
            for val in doc:
                add_docstring(getattr(new, val[0]), val[1].strip())
    except Exception:
        pass


# Based on scitools meshgrid 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import place [as 別名]
def _lazyselect(condlist, choicelist, arrays, default=0):
    """
    Mimic `np.select(condlist, choicelist)`.

    Notice it assumes that all `arrays` are of the same shape, or can be
    broadcasted together.

    All functions in `choicelist` must accept array arguments in the order
    given in `arrays` and must return an array of the same shape as broadcasted
    `arrays`.

    Examples
    --------
    >>> x = np.arange(6)
    >>> np.select([x <3, x > 3], [x**2, x**3], default=0)
    array([  0,   1,   4,   0,  64, 125])

    >>> _lazyselect([x < 3, x > 3], [lambda x: x**2, lambda x: x**3], (x,))
    array([   0.,    1.,    4.,   0.,   64.,  125.])

    >>> a = -np.ones_like(x)
    >>> _lazyselect([x < 3, x > 3],
    ...             [lambda x, a: x**2, lambda x, a: a * x**3],
    ...             (x, a), default=np.nan)
    array([   0.,    1.,    4.,   nan,  -64., -125.])

    """
    arrays = np.broadcast_arrays(*arrays)
    tcode = np.mintypecode([a.dtype.char for a in arrays])
    out = _valarray(np.shape(arrays[0]), value=default, typecode=tcode)
    for index in range(len(condlist)):
        func, cond = choicelist[index], condlist[index]
        if np.all(cond is False):
            continue
        cond, _ = np.broadcast_arrays(cond, arrays[0])
        temp = tuple(np.extract(cond, arr) for arr in arrays)
        np.place(out, cond, func(*temp))
    return out 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import place [as 別名]
def _stats(self, b, moments='mv'):
        mu, mu2, g1, g2 = None, None, None, None
        if 'm' in moments:
            mask = b > 1
            bt = np.extract(mask, b)
            mu = valarray(np.shape(b), value=np.inf)
            np.place(mu, mask, bt / (bt-1.0))
        if 'v' in moments:
            mask = b > 2
            bt = np.extract(mask, b)
            mu2 = valarray(np.shape(b), value=np.inf)
            np.place(mu2, mask, bt / (bt-2.0) / (bt-1.0)**2)
        if 's' in moments:
            mask = b > 3
            bt = np.extract(mask, b)
            g1 = valarray(np.shape(b), value=np.nan)
            vals = 2 * (bt + 1.0) * np.sqrt(bt - 2.0) / ((bt - 3.0) * np.sqrt(bt))
            np.place(g1, mask, vals)
        if 'k' in moments:
            mask = b > 4
            bt = np.extract(mask, b)
            g2 = valarray(np.shape(b), value=np.nan)
            vals = (6.0*np.polyval([1.0, 1.0, -6, -2], bt) /
                    np.polyval([1.0, -7.0, 12.0, 0.0], bt))
            np.place(g2, mask, vals)
        return mu, mu2, g1, g2 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import place [as 別名]
def _pdf(self, x, b):
        # rice.pdf(x, b) = x * exp(-(x**2+b**2)/2) * I[0](x*b)
        #
        # We use (x**2 + b**2)/2 = ((x-b)**2)/2 + xb.
        # The factor of np.exp(-xb) is then included in the i0e function
        # in place of the modified Bessel function, i0, improving
        # numerical stability for large values of xb.
        return x * np.exp(-(x-b)*(x-b)/2.0) * sc.i0e(x*b) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import place [as 別名]
def add_newdoc(place, obj, doc):
    """
    Adds documentation to obj which is in module place.

    If doc is a string add it to obj as a docstring

    If doc is a tuple, then the first element is interpreted as
       an attribute of obj and the second as the docstring
          (method, docstring)

    If doc is a list, then each element of the list should be a
       sequence of length two --> [(method1, docstring1),
       (method2, docstring2), ...]

    This routine never raises an error.

    This routine cannot modify read-only docstrings, as appear
    in new-style classes or built-in functions. Because this
    routine never raises an error the caller must check manually
    that the docstrings were changed.
    """
    try:
        new = getattr(__import__(place, globals(), {}, [obj]), obj)
        if isinstance(doc, str):
            add_docstring(new, doc.strip())
        elif isinstance(doc, tuple):
            add_docstring(getattr(new, doc[0]), doc[1].strip())
        elif isinstance(doc, list):
            for val in doc:
                add_docstring(getattr(new, val[0]), val[1].strip())
    except:
        pass


# Based on scitools meshgrid