Python numpy.divide方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import divide [as 別名]
def SpectralClustering(CKSym, n):
    # This is direct port of JHU vision lab code. Could probably use sklearn SpectralClustering.
    CKSym = CKSym.astype(float)
    N, _ = CKSym.shape
    MAXiter = 1000  # Maximum number of iterations for KMeans
    REPlic = 20  # Number of replications for KMeans

    DN = np.diag(np.divide(1, np.sqrt(np.sum(CKSym, axis=0) + np.finfo(float).eps)))
    LapN = identity(N).toarray().astype(float) - np.matmul(np.matmul(DN, CKSym), DN)
    _, _, vN = np.linalg.svd(LapN)
    vN = vN.T
    kerN = vN[:, N - n:N]
    normN = np.sqrt(np.sum(np.square(kerN), axis=1))
    kerNS = np.divide(kerN, normN.reshape(len(normN), 1) + np.finfo(float).eps)
    km = KMeans(n_clusters=n, n_init=REPlic, max_iter=MAXiter, n_jobs=-1).fit(kerNS)
    return km.labels_ 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import divide [as 別名]
def compute_gradients(self, loss, var_list, **kwargs):
        grads_and_vars = tf.train.AdamOptimizer.compute_gradients(self, loss, var_list, **kwargs)
        grads_and_vars = [(g, v) for g, v in grads_and_vars if g is not None]
        flat_grad = tf.concat([tf.reshape(g, (-1,)) for g, v in grads_and_vars], axis=0)
        shapes = [v.shape.as_list() for g, v in grads_and_vars]
        sizes = [int(np.prod(s)) for s in shapes]

        num_tasks = self.comm.Get_size()
        buf = np.zeros(sum(sizes), np.float32)

        def _collect_grads(flat_grad):
            self.comm.Allreduce(flat_grad, buf, op=MPI.SUM)
            np.divide(buf, float(num_tasks), out=buf)
            return buf

        avg_flat_grad = tf.py_func(_collect_grads, [flat_grad], tf.float32)
        avg_flat_grad.set_shape(flat_grad.shape)
        avg_grads = tf.split(avg_flat_grad, sizes, axis=0)
        avg_grads_and_vars = [(tf.reshape(g, v.shape), v)
                    for g, (_, v) in zip(avg_grads, grads_and_vars)]

        return avg_grads_and_vars 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import divide [as 別名]
def merge(self, tiles: List[np.ndarray], dtype=np.float32):
        if len(tiles) != len(self.crops):
            raise ValueError

        channels = 1 if len(tiles[0].shape) == 2 else tiles[0].shape[2]
        target_shape = self.image_height + self.margin_bottom + self.margin_top, self.image_width + self.margin_right + self.margin_left, channels

        image = np.zeros(target_shape, dtype=np.float64)
        norm_mask = np.zeros(target_shape, dtype=np.float64)

        w = np.dstack([self.weight] * channels)

        for tile, (x, y, tile_width, tile_height) in zip(tiles, self.crops):
            # print(x, y, tile_width, tile_height, image.shape)
            image[y:y + tile_height, x:x + tile_width] += tile * w
            norm_mask[y:y + tile_height, x:x + tile_width] += w

        # print(norm_mask.min(), norm_mask.max())
        norm_mask = np.clip(norm_mask, a_min=np.finfo(norm_mask.dtype).eps, a_max=None)
        normalized = np.divide(image, norm_mask).astype(dtype)
        crop = self.crop_to_orignal_size(normalized)
        return crop 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import divide [as 別名]
def generate_cRM(Y,S):
    '''

    :param Y: mixed/noisy stft
    :param S: clean stft
    :return: structed cRM
    '''
    M = np.zeros(Y.shape)
    epsilon = 1e-8
    # real part
    M_real = np.multiply(Y[:,:,0],S[:,:,0])+np.multiply(Y[:,:,1],S[:,:,1])
    square_real = np.square(Y[:,:,0])+np.square(Y[:,:,1])
    M_real = np.divide(M_real,square_real+epsilon)
    M[:,:,0] = M_real
    # imaginary part
    M_img = np.multiply(Y[:,:,0],S[:,:,1])-np.multiply(Y[:,:,1],S[:,:,0])
    square_img = np.square(Y[:,:,0])+np.square(Y[:,:,1])
    M_img = np.divide(M_img,square_img+epsilon)
    M[:,:,1] = M_img
    return M 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import divide [as 別名]
def cRM_tanh_compress(M,K=10,C=0.1):
    '''
    Recall that the irm takes on vlaues in the range[0,1],compress the cRM with hyperbolic tangent
    :param M: crm (298,257,2)
    :param K: parameter to control the compression
    :param C: parameter to control the compression
    :return crm: compressed crm
    '''

    numerator = 1-np.exp(-C*M)
    numerator[numerator == inf] = 1
    numerator[numerator == -inf] = -1
    denominator = 1+np.exp(-C*M)
    denominator[denominator == inf] = 1
    denominator[denominator == -inf] = -1
    crm = K * np.divide(numerator,denominator)

    return crm 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import divide [as 別名]
def axisangle_from_rotm(R):
  # logarithm of rotation matrix
  # R = R.reshape(-1,3,3)
  # tr = np.trace(R, axis1=1, axis2=2)
  # phi = np.arccos(np.clip((tr - 1) / 2, -1, 1))
  # scale = np.zeros_like(phi)
  # div = 2 * np.sin(phi)
  # np.divide(phi, div, out=scale, where=np.abs(div) > 1e-6)
  # A = (R - R.transpose(0,2,1)) * scale.reshape(-1,1,1)
  # aa = np.stack((A[:,2,1], A[:,0,2], A[:,1,0]), axis=1)
  # return aa.squeeze()
  R = R.reshape(-1,3,3)
  omega = np.empty((R.shape[0], 3), dtype=R.dtype)
  omega[:,0] = R[:,2,1] - R[:,1,2]
  omega[:,1] = R[:,0,2] - R[:,2,0]
  omega[:,2] = R[:,1,0] - R[:,0,1]
  r = np.linalg.norm(omega, axis=1).reshape(-1,1)
  t = np.trace(R, axis1=1, axis2=2).reshape(-1,1)
  omega = np.arctan2(r, t-1) * omega
  aa = np.zeros_like(omega)
  np.divide(omega, r, out=aa, where=r != 0)
  return aa.squeeze() 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import divide [as 別名]
def test_NotImplemented_not_returned(self):
        # See gh-5964 and gh-2091. Some of these functions are not operator
        # related and were fixed for other reasons in the past.
        binary_funcs = [
            np.power, np.add, np.subtract, np.multiply, np.divide,
            np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or,
            np.bitwise_xor, np.left_shift, np.right_shift, np.fmax,
            np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2,
            np.logical_and, np.logical_or, np.logical_xor, np.maximum,
            np.minimum, np.mod,
            np.greater, np.greater_equal, np.less, np.less_equal,
            np.equal, np.not_equal]

        a = np.array('1')
        b = 1
        c = np.array([1., 2.])
        for f in binary_funcs:
            assert_raises(TypeError, f, a, b)
            assert_raises(TypeError, f, c, a) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import divide [as 別名]
def test_warnings(self):
        # test warning code path
        with warnings.catch_warnings(record=True) as w:
            warnings.simplefilter("always")
            with np.errstate(all="warn"):
                np.divide(1, 0.)
                assert_equal(len(w), 1)
                assert_("divide by zero" in str(w[0].message))
                np.array(1e300) * np.array(1e300)
                assert_equal(len(w), 2)
                assert_("overflow" in str(w[-1].message))
                np.array(np.inf) - np.array(np.inf)
                assert_equal(len(w), 3)
                assert_("invalid value" in str(w[-1].message))
                np.array(1e-300) * np.array(1e-300)
                assert_equal(len(w), 4)
                assert_("underflow" in str(w[-1].message)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import divide [as 別名]
def test_td64arr_rmul_numeric_array(self, box_with_array, vector, dtype):
        # GH#4521
        # divide/multiply by integers
        xbox = get_upcast_box(box_with_array, vector)

        tdser = pd.Series(['59 Days', '59 Days', 'NaT'], dtype='m8[ns]')
        vector = vector.astype(dtype)

        expected = Series(['1180 Days', '1770 Days', 'NaT'],
                          dtype='timedelta64[ns]')

        tdser = tm.box_expected(tdser, box_with_array)
        expected = tm.box_expected(expected, xbox)

        result = tdser * vector
        tm.assert_equal(result, expected)

        result = vector * tdser
        tm.assert_equal(result, expected) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import divide [as 別名]
def planck(f, T):
    """Calculate black body radiation for given frequency and temperature.

    Parameters:
        f (float or ndarray): Frquencies [Hz].
        T (float or ndarray): Temperature [K].

    Returns:
        float or ndarray: Radiances.

    """
    c = constants.speed_of_light
    h = constants.planck
    k = constants.boltzmann

    return 2 * h * f**3 / (c**2 * (np.exp(np.divide(h * f, (k * T))) - 1)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import divide [as 別名]
def planck_wavelength(l, T):
    """Calculate black body radiation for given wavelength and temperature.

    Parameters:
        l (float or ndarray): Wavelength [m].
        T (float or ndarray): Temperature [K].

    Returns:
        float or ndarray: Radiances.

    """
    c = constants.speed_of_light
    h = constants.planck
    k = constants.boltzmann

    return 2 * h * c**2 / (l**5 * (np.exp(np.divide(h * c, (l * k * T))) - 1)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import divide [as 別名]
def planck_wavenumber(n, T):
    """Calculate black body radiation for given wavenumber and temperature.

    Parameters:
        n (float or ndarray): Wavenumber.
        T (float or ndarray): Temperature [K].

    Returns:
        float or ndarray: Radiances.

    """
    c = constants.speed_of_light
    h = constants.planck
    k = constants.boltzmann

    return 2 * h * c**2 * n**3 / (np.exp(np.divide(h * c * n, (k * T))) - 1) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import divide [as 別名]
def rayleighjeans_wavelength(l, T):
    """Calculates the Rayleigh-Jeans approximation of the Planck function.

     Calculates the approximation of the Planck function for given
     wavelength and temperature.

     Parameters:
        l (float or ndarray): Wavelength [m].
        T (float or ndarray): Temperature [K].

     Returns:
        float or ndarray: Radiance [W/(m2*Hz*sr)].

    """
    c = constants.speed_of_light
    k = constants.boltzmann

    return np.divide(2 * c * k * T, l**4) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import divide [as 別名]
def unitvec(vector, ax=1):
    v=vector*vector
    if len(vector.shape)==1:
        sqrtv=np.sqrt(np.sum(v))
    elif len(vector.shape)==2:
        sqrtv=np.sqrt([np.sum(v, axis=ax)])
    else:
        raise Exception('It\'s too large.')
    if ax==1:
        result=np.divide(vector,sqrtv.T)
    elif ax==0:
        result=np.divide(vector,sqrtv)
    return result 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import divide [as 別名]
def test_evaluate(self):
    (average_precision_per_class, mean_ap, precisions_per_class,
     recalls_per_class, corloc_per_class,
     mean_corloc) = self.od_eval.evaluate()
    expected_precisions_per_class = [np.array([0, 0.5], dtype=float),
                                     np.array([], dtype=float),
                                     np.array([0], dtype=float)]
    expected_recalls_per_class = [
        np.array([0, 1. / 3.], dtype=float), np.array([], dtype=float),
        np.array([0], dtype=float)
    ]
    expected_average_precision_per_class = np.array([1. / 6., 0, 0],
                                                    dtype=float)
    expected_corloc_per_class = np.array([0, np.divide(0, 0), 0], dtype=float)
    expected_mean_ap = 1. / 18
    expected_mean_corloc = 0.0
    for i in range(self.od_eval.num_class):
      self.assertTrue(np.allclose(expected_precisions_per_class[i],
                                  precisions_per_class[i]))
      self.assertTrue(np.allclose(expected_recalls_per_class[i],
                                  recalls_per_class[i]))
    self.assertTrue(np.allclose(expected_average_precision_per_class,
                                average_precision_per_class))
    self.assertTrue(np.allclose(expected_corloc_per_class, corloc_per_class))
    self.assertAlmostEqual(expected_mean_ap, mean_ap)
    self.assertAlmostEqual(expected_mean_corloc, mean_corloc)