Python numpy.vectorize方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import vectorize [as 別名]
def test_static_nonlinearity(self):
        # Linear dynamical system
        linsys = self.siso_linsys
        ioslin = ios.LinearIOSystem(linsys)

        # Nonlinear saturation
        sat = lambda u: u if abs(u) < 1 else np.sign(u)
        sat_output = lambda t, x, u, params: sat(u)
        nlsat =  ios.NonlinearIOSystem(None, sat_output, inputs=1, outputs=1)

        # Set up parameters for simulation
        T, U, X0 = self.T, 2 * self.U, self.X0
        Usat = np.vectorize(sat)(U)

        # Make sure saturation works properly by comparing linear system with
        # saturated input to nonlinear system with saturation composition
        lti_t, lti_y, lti_x = ct.forced_response(linsys, T, Usat, X0)
        ios_t, ios_y, ios_x = ios.input_output_response(
            ioslin * nlsat, T, U, X0, return_x=True)
        np.testing.assert_array_almost_equal(lti_t, ios_t)
        np.testing.assert_array_almost_equal(lti_y, ios_y, decimal=2) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import vectorize [as 別名]
def test_keywords2_ticket_2100(self):
        # Test kwarg support: enhancement ticket 2100

        def foo(a, b=1):
            return a + b

        f = vectorize(foo)
        args = np.array([1, 2, 3])
        r1 = f(a=args)
        r2 = np.array([2, 3, 4])
        assert_array_equal(r1, r2)
        r1 = f(b=1, a=args)
        assert_array_equal(r1, r2)
        r1 = f(args, b=2)
        r2 = np.array([3, 4, 5])
        assert_array_equal(r1, r2) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import vectorize [as 別名]
def dict_lookup(dict_, keys, default=None):
    """Looks up :attr:`keys` in the dict, returns the corresponding values.

    The :attr:`default` is used for keys not present in the dict.

    Args:
        dict_ (dict): A dictionary for lookup.
        keys: A numpy array or a (possibly nested) list of keys.
        default (optional): Value to be returned when a key is not in
            :attr:`dict_`. Error is raised if :attr:`default` is not given and
            key is not in the dict.

    Returns:
        A numpy array of values with the same structure as :attr:`keys`.

    Raises:
        TypeError: If key is not in :attr:`dict_` and :attr:`default` is `None`.
    """
    return np.vectorize(lambda x: dict_.get(x, default))(keys) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import vectorize [as 別名]
def xindex(array, row_num, col_num=None, area_num=1):
    is_reference = isinstance(array, Ranges)
    if is_reference:
        arrays = [Ranges((rng,), array.values).value for rng in array.ranges]
    else:
        arrays = [array]

    row_num, col_num, area_num = parse_ranges(row_num, col_num, area_num)[0]

    res = np.vectorize(_index, excluded={0}, otypes=[object])(
        arrays, row_num, col_num, area_num, is_reference,
        isinstance(row_num, np.ndarray)
    )
    if not res.shape:
        res = res.reshape(1, 1)
    return res.view(Array) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import vectorize [as 別名]
def testRelativePositionalEmbeddingLayer(self):
    with self.session(use_gpu=False):
      radius = 3
      p = layers.RelativePositionalEmbeddingLayer.Params().Set(
          name='rel_position_emb', radius=radius, dim=4)
      layer = p.Instantiate()
      indices = np.array([-5, -2, 0, 1, 4], dtype=np.int32)
      pos_emb = layer.FPropDefaultTheta(tf.convert_to_tensor(indices))

      self.evaluate(tf.global_variables_initializer())
      actual_pos_emb, full_emb = self.evaluate([pos_emb, layer.vars.w])

      clipped_indices = np.vectorize(lambda x: max(-radius, min(radius, x)))(
          indices) + radius
      expected_output = np.take_along_axis(full_emb,
                                           np.expand_dims(clipped_indices, -1),
                                           0)
      print('expected_position_embs:', expected_output)
      print('actual_position_embs:', actual_pos_emb)
      self.assertAllClose(actual_pos_emb, expected_output) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import vectorize [as 別名]
def plot3d(self, scale=0.32):
		r"""Plot 3d scatter plot of benchmark function.

		Args:
			scale (float): Scale factor for points.
		"""
		fig = plt.figure()
		ax = Axes3D(fig)
		func = self.function()
		Xr, Yr = arange(self.Lower, self.Upper, scale), arange(self.Lower, self.Upper, scale)
		X, Y = meshgrid(Xr, Yr)
		Z = vectorize(self.__2dfun)(X, Y, func)
		ax.plot_surface(X, Y, Z, rstride=8, cstride=8, alpha=0.3)
		ax.contourf(X, Y, Z, zdir='z', offset=-10, cmap=cm.coolwarm)
		ax.set_xlabel('X')
		ax.set_ylabel('Y')
		ax.set_zlabel('Z')
		plt.show()

# vim: tabstop=3 noexpandtab shiftwidth=3 softtabstop=3 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import vectorize [as 別名]
def make_polynomial(degree=3, n_samples=100, bias=0.0, noise=0.0,
                    return_coefs=False, random_state=None):
    """
    Generate a noisy polynomial for a regression problem

    Examples
    --------
    >>> X, y, coefs = make_polynomial(degree=3, n_samples=200, noise=.5,
    ...                               return_coefs=True, random_state=1)
    """
    generator = check_random_state(random_state)

    # TODO: Add arguments to support other priors
    coefs = generator.randn(degree + 1)
    pows = np.arange(degree + 1)
    poly = np.vectorize(lambda x: np.sum(coefs * x ** pows))
    X, y = make_regression(poly, n_samples=n_samples, bias=bias, noise=noise,
                           random_state=random_state)
    if return_coefs:
        return X, y, coefs

    return X, y 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import vectorize [as 別名]
def _munp(self, n, beta, m):
        """
        Returns the n-th non-central moment of the crystalball function.
        """
        N = 1.0 / (m/beta / (m-1) * np.exp(-beta**2 / 2.0) + _norm_pdf_C * _norm_cdf(beta))

        def n_th_moment(n, beta, m):
            """
            Returns n-th moment. Defined only if n+1 < m
            Function cannot broadcast due to the loop over n
            """
            A = (m/beta)**m * np.exp(-beta**2 / 2.0)
            B = m/beta - beta
            rhs = 2**((n-1)/2.0) * sc.gamma((n+1)/2) * (1.0 + (-1)**n * sc.gammainc((n+1)/2, beta**2 / 2))
            lhs = np.zeros(rhs.shape)
            for k in range(n + 1):
                lhs += sc.binom(n, k) * B**(n-k) * (-1)**k / (m - k - 1) * (m/beta)**(-m + k + 1)
            return A * lhs + rhs

        return N * _lazywhere(np.atleast_1d(n + 1 < m),
                              (n, beta, m),
                              np.vectorize(n_th_moment, otypes=[np.float]),
                              np.inf) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import vectorize [as 別名]
def get_forces(self):
        # Return the force vector for the problem
        topx_to_id = np.vectorize(
            lambda x: xy_to_id(x, 0, self.nelx, self.nely))
        topx = 2 * topx_to_id(np.arange((self.nelx + 1) // 2)) + 1
        f = np.zeros((2 * (self.nelx + 1) * (self.nely + 1), 1))
        f[topx, 0] = -100
        return f 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import vectorize [as 別名]
def get_fixed_nodes(self):
        """ Return a list of fixed nodes for the problem. """
        x = np.arange(self.passive_min_x)
        topx_to_id = np.vectorize(
            lambda x: xy_to_id(x, 0, self.nelx, self.nely))
        ids = topx_to_id(x)
        fixed = np.union1d(2 * ids, 2 * ids + 1)
        return fixed 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import vectorize [as 別名]
def get_passive_elements(self):
        X, Y = np.mgrid[self.passive_min_x:self.passive_max_x + 1,
            self.passive_min_y:self.passive_max_y]
        pairs = np.vstack([X.ravel(), Y.ravel()]).T
        passive_to_ids = np.vectorize(lambda pair: xy_to_id(*pair,
            nelx=self.nelx - 1, nely=self.nely - 1), signature="(m)->()")
        return passive_to_ids(pairs) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import vectorize [as 別名]
def __getitem__(self, index):
        raw_data = np.fromfile(self.im_idx[index], dtype=np.float32).reshape((-1, 4))
        if self.imageset == 'test':
            annotated_data = np.expand_dims(np.zeros_like(raw_data[:,0],dtype=int),axis=1)
        else:
            annotated_data = np.fromfile(self.im_idx[index].replace('velodyne','labels')[:-3]+'label', dtype=np.int32).reshape((-1,1))
            annotated_data = annotated_data & 0xFFFF #delete high 16 digits binary
            annotated_data = np.vectorize(self.learning_map.__getitem__)(annotated_data)
        data_tuple = (raw_data[:,:3], annotated_data.astype(np.uint8))
        if self.return_ref:
            data_tuple += (raw_data[:,3],)
        return data_tuple 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import vectorize [as 別名]
def getTransform(img, pc, s, m_p, mean_m):
	z = np.copy(img)
	p, m = getLandmarks(img, pc, m_p)

	#using img, p, m, s, mean_m get the normalized image
	p_1, p_2 = p[0], p[1]
	s_1, s_2 = s[0], s[1]

	#histogram values at locations (pc + landmarks)
	m = [p_1] + list(m) + [p_2]
	#map scale corresponding to these values
	mean_m = [s_1] + list(mean_m) + [s_2]
	new_img = np.zeros_like(img, dtype=np.int64)
	hist_indices = np.zeros_like(img, dtype=np.int64)

	hist_indices = np.copy(new_img)

	for m_ in m:
		hist_indices += (img > m_).astype(int)

	hist_indices = np.clip(hist_indices, 1, len(m) - 1, out=hist_indices)

	indexer_m = lambda v: m[v]
	indexer_mm = lambda v: mean_m[v]
	f_m = np.vectorize(indexer_m)
	f_mm = np.vectorize(indexer_mm)
	
	new_p_1 = f_m(hist_indices - 1)
	new_p_2 = f_m(hist_indices)
	new_s_1 = f_mm(hist_indices - 1)
	new_s_2 = f_mm(hist_indices)
	
	new_img = mapLandmarksVec([new_p_1, new_p_2], [new_s_1, new_s_2], img)
	
	new_img = np.clip(new_img, s_1-1, s_2+1, out=new_img)
	
	return new_img

################################################################## 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import vectorize [as 別名]
def calc_EVPOC(self):
        Comp = self.Completeness

        bins = 1000
        # xedges is array of separation values for interpolant
        xedges = np.linspace(0., Comp.PlanetPopulation.rrange[1].value, bins)*\
                Comp.PlanetPopulation.arange.unit
        xedges = xedges.to('AU').value

        # yedges is array of delta magnitude values for interpolant
        ymin = np.round(-2.5*np.log10(float(Comp.PlanetPopulation.prange[1]*\
                Comp.PlanetPopulation.Rprange[1]/Comp.PlanetPopulation.rrange[0])**2))
        ymax = np.round(-2.5*np.log10(float(Comp.PlanetPopulation.prange[0]*\
                Comp.PlanetPopulation.Rprange[0]/Comp.PlanetPopulation.rrange[1])**2*1e-11))
        yedges = np.linspace(ymin, ymax, bins)

        # number of planets for each Monte Carlo simulation
        nplan = int(np.min([1e6,Comp.Nplanets]))
        # number of simulations to perform (must be integer)
        steps = int(Comp.Nplanets/nplan)
        
        Cpath = os.path.join(Comp.classpath, Comp.filename+'.comp')
        H, xedges, yedges = self.genC(Cpath, nplan, xedges, yedges, steps)
        EVPOCpdf = interpolate.RectBivariateSpline(xedges, yedges, H.T)
        EVPOC = np.vectorize(EVPOCpdf.integral)

        self.EVPOC = EVPOC 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import vectorize [as 別名]
def predict_proba(self, X):
        f = np.vectorize(self._platt_func)
        raw_predictions = self.decision_function(X)
        platt_predictions = f(raw_predictions).reshape(-1, 1)
        prob_positive = platt_predictions / platt_predictions.sum(axis=1)[:, None]
        prob_negative = 1.0 - prob_positive
        probabilities = np.hstack([prob_negative, prob_positive])
        return probabilities