numpy.power

Here are the examples of the python api numpy.power taken from open source projects. By voting up you can indicate which examples are most useful and appropriate.

170 Examples 7

Example 1

Project: pyeq3 Source File: YieldDensity.py
Function: calculatemodelpredictions
    def CalculateModelPredictions(self, inCoeffs, inDataCacheDictionary):
        x_in = inDataCacheDictionary['X'] # only need to perform this dictionary look-up once
        
        a = inCoeffs[0]
        b = inCoeffs[1]
        c = inCoeffs[2]

        try:
            temp = x_in / numpy.power((a + b*x_in), (-1.0/c))
            return self.extendedVersionHandler.GetAdditionalModelPredictions(temp, inCoeffs, inDataCacheDictionary, self)
        except:
            return numpy.ones(len(inDataCacheDictionary['DependentData'])) * 1.0E300

Example 2

Project: misvm Source File: kernel.py
Function: polynomial
def polynomial(p):
    """General polynomial kernel (1 + x'*y)^p"""

    def p_kernel(x, y):
        return np.power(1e0 + x * y.T, p)

    return p_kernel

Example 3

Project: statsmodels Source File: gam.py
Function: estimate_scale
    def estimate_scale(self, Y=None):
        """
        Return Pearson\'s X^2 estimate of scale.
        """

        if Y is None:
            Y = self.Y
        resid = Y - self.results.mu
        return (np.power(resid, 2) / self.family.variance(self.results.mu)).sum() \
                    / self.df_resid   #TODO check this

Example 4

Project: pyeq3 Source File: Engineering.py
Function: calculatemodelpredictions
    def CalculateModelPredictions(self, inCoeffs, inDataCacheDictionary):
        x_in = inDataCacheDictionary['X'] # only need to perform this dictionary look-up once
        
        Youngs_Modulus = inCoeffs[0]
        K = inCoeffs[1]
        n = inCoeffs[2]

        try:
            temp = (x_in / Youngs_Modulus) +  numpy.power(x_in / K, 1.0 / n)
            return self.extendedVersionHandler.GetAdditionalModelPredictions(temp, inCoeffs, inDataCacheDictionary, self)
        except:
            return numpy.ones(len(inDataCacheDictionary['DependentData'])) * 1.0E300

Example 5

Project: deep_recommend_system Source File: fft_ops_test.py
Function: test_basic
  def testBasic(self):
    for rank in VALID_FFT_RANKS:
      for dims in xrange(rank, rank + 3):
        self._Compare(
            np.mod(
                np.arange(np.power(4, dims)), 10).reshape((4,) * dims), rank)

Example 6

Project: sherpa Source File: test_stats.py
    @staticmethod
    def my_simulstat(data, model, staterror, *args, **kwargs):
        data_size = kwargs['extra_args']['data_size']
        data1 = data[:data_size[0]]
        data2 = data[data_size[0]:]
        model1 = model[:data_size[0]]
        model2 = model[data_size[0]:]
        staterror1 = staterror[:data_size[0]]
        staterror2 = staterror[data_size[0]:]
        mystat1 = Chi2DataVar()
        mystat2 = Chi2DataVar()
        stat1, fvec1 = mystat1.calc_stat(data1, model1, staterror1)
        stat2, fvec2 = mystat2.calc_stat(data2, model2, staterror2)
        fvec = numpy.power((data - model) / staterror, 2)
        stat = numpy.sum(fvec)
        # print stat1 + stat2 - stat
        return (stat, fvec)
        return (stat1 + stat2, numpy.append(fvec1, fvec2))

Example 7

Project: cvxpy Source File: quadratic.py
    def _coeffs_power(self, expr):
        if expr.p == 1:
            return self.get_coeffs(expr.args[0])
        elif expr.p == 2:
            (_, A, b) = self._coeffs_affine(expr.args[0])
            Ps = [(A[i, :].T*A[i, :]).tocsr() for i in range(A.shape[0])]
            Q = 2*(sp.diags(b, 0)*A).tocsr()
            R = np.power(b, 2)
            return (Ps, Q, R)
        else:
            raise Exception("Error while processing power(x, %f)." % expr.p)

Example 8

Project: pyflux Source File: nllt.py
    def state_likelihood(self, beta, alpha):
        """ Returns likelihood of the states given the variance latent variables

        Parameters
        ----------
        beta : np.array
            Contains untransformed starting values for latent variables

        alpha : np.array
            State matrix
        
        Returns
        ----------
        State likelihood
        """
        _, _, _, Q = self._ss_matrices(beta)
        residuals_1 = alpha[0][1:alpha[0].shape[0]]-alpha[0][0:alpha[0].shape[0]-1]
        residuals_2 = alpha[1][1:alpha[1].shape[0]]-alpha[1][0:alpha[1].shape[0]-1]
        return np.sum(ss.norm.logpdf(residuals_1,loc=0,scale=np.power(Q[0][0],0.5))) + np.sum(ss.norm.logpdf(residuals_2,loc=0,scale=np.power(Q[1][1],0.5)))

Example 9

Project: oq-hazardlib Source File: edwards_fah_2013a.py
Function: compute_term_3
    def _compute_term_3(self, C, mag, R):
        """
        (a12 + a13.*M + a14.*M.*M + a15.*M.*M.*M).*(d(r).^2)
        """
        return (
            (C['a12'] + C['a13'] * mag + C['a14'] * np.power(mag, 2) +
             C['a15'] * np.power(mag, 3)) * np.power(R, 2)
        )

Example 10

Project: scipy Source File: models.py
def _poly_fjacd(B, x, powers):
    b = B[1:]
    b.shape = (b.shape[0], 1)

    b = b * powers

    return np.sum(b * np.power(x, powers-1),axis=0)

Example 11

Project: pyeq2 Source File: Power.py
Function: calculatemodelpredictions
    def CalculateModelPredictions(self, inCoeffs, inDataCacheDictionary):
        x_in = inDataCacheDictionary['X'] # only need to perform this dictionary look-up once
        
        a = inCoeffs[0]
        b = inCoeffs[1]
        c = inCoeffs[2]

        try:
            temp = numpy.power(a + b * x_in, c)
            return self.extendedVersionHandler.GetAdditionalModelPredictions(temp, inCoeffs, inDataCacheDictionary, self)
        except:
            return numpy.ones(len(inDataCacheDictionary['DependentData'])) * 1.0E300

Example 12

Project: orange Source File: orngDimRed.py
def VarianceScaling(vector,param=None,inverse=0):
    if param == None:
        (v,m) = Centering(vector)
        s = numpy.sqrt(numpy.average(numpy.power(v,2)))
        if s > 1e-6:
            s = 1.0/s
    else:
        (m,s) = param
        if inverse == 0:
            (v,m_) = Centering(vector,m)
        else:
            v = Centering(vector,m,1)
    if inverse == 0:
        return (s*v,(m,s))
    else:
        return s/v

Example 13

Project: SHARPpy Source File: thermo.py
def thalvl(theta, t):
    '''
    Returns the level (hPa) of a parcel.

    Parameters
    ----------
    theta : number, numpy array
        Potential temperature of the parcel (C)
    t : number, numpy array
        Temperature of the parcel (C)

    Returns
    -------
    Pressure Level (hPa [float]) of the parcel
    '''

    t = t + ZEROCNK
    theta = theta + ZEROCNK
    return 1000. / (np.power((theta / t),(1./ROCP)))

Example 14

Project: pyeq3 Source File: YieldDensity.py
Function: calculatemodelpredictions
    def CalculateModelPredictions(self, inCoeffs, inDataCacheDictionary):
        x_in = inDataCacheDictionary['X'] # only need to perform this dictionary look-up once
        
        a = inCoeffs[0]
        b = inCoeffs[1]
        c = inCoeffs[2]

        try:
            temp = 1.0 / numpy.power((a + b*x_in), (-1.0/c))
            return self.extendedVersionHandler.GetAdditionalModelPredictions(temp, inCoeffs, inDataCacheDictionary, self)
        except:
            return numpy.ones(len(inDataCacheDictionary['DependentData'])) * 1.0E300

Example 15

Project: pyflux Source File: scores.py
    @staticmethod
    def mu_score(y,loc,scale,shape,skewness):
        try:
            if (y-loc)>=0:
                return (((shape+1.0)*power(y-loc,2))/float(power(skewness,2)*shape*exp(scale) + power(y-loc,2))) - 1.0
            else:
                return (((shape+1.0)*power(y-loc,2))/float(power(skewness,-2)*shape*exp(scale) + power(y-loc,2))) - 1.0    
        except:
            return -1.0

Example 16

Project: pyKriging Source File: coKriging.py
Function: cheap
def cheap(X):

    A=0.5
    B=10
    C=-5
    D=0

    print X
    print ((X+D)*6-2)
    return A*np.power( ((X+D)*6-2), 2 )*np.sin(((X+D)*6-2)*2)+((X+D)-0.5)*B+C

Example 17

Project: statsmodels Source File: links.py
    def inverse_deriv(self, z):
        """
        Derivative of the inverse of the power transform

        Parameters
        ----------
        z : array-like
            `z` is usually the linear predictor for a GLM or GEE model.

        Returns
        -------
        g^(-1)'(z) : array
            The value of the derivative of the inverse of the power transform
        function
        """
        return np.power(z, (1 - self.power)/self.power) / self.power

Example 18

Project: pyunlocbox Source File: functions.py
    def _eval(self, x):
        if self.dim >= 2:
            y = 0
            grads = []
            grads = op.grad(x, dim=self.dim, **self.kwargs)
            for g in grads:
                y += np.power(abs(g), 2)
            y = np.sqrt(y)
            return np.sum(y)

        if self.dim == 1:
            dx = op.grad(x, dim=self.dim, **self.kwargs)
            y = np.sum(np.abs(dx), axis=0)
            return np.sum(y)

Example 19

Project: chainer Source File: optimizer.py
def exponential_decay_noise(xp, shape, dtype, hook, opt):
    """Time-dependent annealed Gaussian noise function from the paper:

    `Adding Gradient Noise Improves Learning for Very Deep Networks
    <http://arxiv.org/pdf/1511.06807>`_.
    """
    std = numpy.sqrt(hook.eta / numpy.power(1 + opt.t, 0.55))
    return xp.random.normal(0, std, shape).astype(dtype)

Example 20

Project: oq-hazardlib Source File: edwards_fah_2013a.py
    def _compute_term_2(self, C, mag, R):
        """
        (a8 + a9.*M + a10.*M.*M + a11.*M.*M.*M).*d(r)
        """
        return (
            (C['a8'] + C['a9'] * mag + C['a10'] * np.power(mag, 2) +
             C['a11'] * np.power(mag, 3)) * R
        )

Example 21

Project: GromacsWrapper Source File: observables.py
Function: pow
    def __pow__(self, other, z=None):
        """x.__pow__(y)  <-->  x**y"""
        if not z is None:
            raise NotImplementedError("only pow(self, y) implemented, not pow(self,y,z)")
        x,dx = self.value, self.error
        y,dy,yqid = self._astuple(other)
        if self.isSame(other):
            # not sure if error correct for a**a**a ...
            f = numpy.power(x, y)
            error = numpy.abs(dx*f*(1+numpy.log(x)))
            qid = self.qid
        else:
            f = numpy.power(x,y)
            error = self._dist(dx*f*y/x, dy*f*numpy.log(x))
            qid = [self.qid, yqid]
        return QuantityWithError(f, error, qid=qid)

Example 22

Project: GromacsWrapper Source File: observables.py
Function: rpow
    def __rpow__(self, other, z=None):
        """x.__rpow__(y)  <-->  y**x"""
        if not z is None:
            raise NotImplementedError("only rpow(self, y) implemented, not rpow(self,y,z)")
        x,dx = self.value, self.error
        y,dy,yqid = self._astuple(other)
        if self.isSame(other):
            # not sure if error correct for a**a**a ...
            f = numpy.power(y, x)
            error = numpy.abs(dy*f*(1+numpy.log(y)))
            qid = self.qid
        else:
            f = numpy.power(y, x)
            error = self._dist(dy*f*x/y, dx*f*numpy.log(y))
            qid = [self.qid, yqid]
        return QuantityWithError(f, error, qid=qid)

Example 23

Project: imageqa-public Source File: func.py
def meanSqErr(Y, T, weights=None):
    diff =  Y - T.reshape(Y.shape)
    diff2 = np.sum(np.power(diff, 2), axis=-1)
    if weights is not None:
        diff2 *= weights
        weights = weights.reshape(weights.shape[0], 1)
        diff *= weights
    E = 0.5 * np.sum(diff2) / float(Y.shape[0])
    dEdY = diff / float(Y.shape[0])
    return E, dEdY

Example 24

Project: orange Source File: orngDimRed.py
    def __init__(self, data, components=1):
        (u,d,v) = LinearAlgebra.svd(data)
        self.loading = u                # transformed data points
        self.variance = d               # principal components' variance
        self.factors = v                # the principal basis
        d2 = numpy.power(d,2)
        s = numpy.sum(d2)
        if s > 1e-6:
            s = d2/s
        else:
            s = 1.0
        self.R_squared = s # percentage of total variance explained by individual components

Example 25

Project: scipy Source File: _discrete_distns.py
Function: stats
    def _stats(self, p):
        r = special.log1p(-p)
        mu = p / (p - 1.0) / r
        mu2p = -p / r / (p - 1.0)**2
        var = mu2p - mu*mu
        mu3p = -p / r * (1.0+p) / (1.0 - p)**3
        mu3 = mu3p - 3*mu*mu2p + 2*mu**3
        g1 = mu3 / np.power(var, 1.5)

        mu4p = -p / r * (
            1.0 / (p-1)**2 - 6*p / (p - 1)**3 + 6*p*p / (p-1)**4)
        mu4 = mu4p - 4*mu3p*mu + 6*mu2p*mu*mu - 3*mu**4
        g2 = mu4 / var**2 - 3.0
        return mu, var, g1, g2

Example 26

Project: pyeq2 Source File: Power.py
Function: calculatemodelpredictions
    def CalculateModelPredictions(self, inCoeffs, inDataCacheDictionary):
        x_in = inDataCacheDictionary['X'] # only need to perform this dictionary look-up once
        
        a = inCoeffs[0]
        b = inCoeffs[1]

        try:
            temp = numpy.power(a + x_in, b)
            return self.extendedVersionHandler.GetAdditionalModelPredictions(temp, inCoeffs, inDataCacheDictionary, self)
        except:
            return numpy.ones(len(inDataCacheDictionary['DependentData'])) * 1.0E300

Example 27

Project: pyeq2 Source File: NIST.py
Function: calculatemodelpredictions
    def CalculateModelPredictions(self, inCoeffs, inDataCacheDictionary):
        x_in = inDataCacheDictionary['X'] # only need to perform this dictionary look-up once
        
        a = inCoeffs[0]
        b = inCoeffs[1]
        c = inCoeffs[2]

        try:
            temp = a * numpy.power(b + x_in, -1.0 / c)
            return self.extendedVersionHandler.GetAdditionalModelPredictions(temp, inCoeffs, inDataCacheDictionary, self)
        except:
            return numpy.ones(len(inDataCacheDictionary['DependentData'])) * 1.0E300

Example 28

Project: sfepy Source File: coefs_phononic.py
Function: get_coefs
    def get_coefs(self, freq):
        """
        Get frequency-dependent coefficients.
        """
        eigs = self.eigs

        f2 = freq*freq
        aux = (f2 - self.gamma * eigs)
        num = f2 * aux
        denom = aux*aux + f2*(self.eta*self.eta)*nm.power(eigs, 2.0)
        return num, denom

Example 29

Project: pyeq2 Source File: Optical.py
Function: calculatemodelpredictions
    def CalculateModelPredictions(self, inCoeffs, inDataCacheDictionary):
        XSQPLUSYSQ = inDataCacheDictionary['XSQPLUSYSQ'] # only need to perform this dictionary look-up once
        XSQPLUSYSQ_POW4_3D = inDataCacheDictionary['XSQPLUSYSQ_POW4_3D'] # only need to perform this dictionary look-up once
        XSQPLUSYSQ_POW6_3D = inDataCacheDictionary['XSQPLUSYSQ_POW6_3D'] # only need to perform this dictionary look-up once
        XSQPLUSYSQ_POW8_3D = inDataCacheDictionary['XSQPLUSYSQ_POW8_3D'] # only need to perform this dictionary look-up once
        
        k = inCoeffs[0]
        r = inCoeffs[1]
        A4 = inCoeffs[2]
        A6 = inCoeffs[3]
        A8 = inCoeffs[4]

        try:
            s_sq = XSQPLUSYSQ
            s_over_r = numpy.power(s_sq) / r
            temp = (s_sq / r) / (1.0 + numpy.sqrt(1.0 - (k + 1.0) * s_over_r * s_over_r)) + A4 * XSQPLUSYSQ_POW4_3D + A6 * XSQPLUSYSQ_POW6_3D + A8 * XSQPLUSYSQ_POW8_3D
            return self.extendedVersionHandler.GetAdditionalModelPredictions(temp, inCoeffs, inDataCacheDictionary, self)
        except:
            return numpy.ones(len(inDataCacheDictionary['DependentData'])) * 1.0E300

Example 30

Project: tfdeploy Source File: tfdeploy.py
Function: pow
@Operation.factory
def Pow(a, b):
    """
    Power op.
    """
    return np.power(a, b),

Example 31

Project: pyeq3 Source File: Exponential.py
Function: calculatemodelpredictions
    def CalculateModelPredictions(self, inCoeffs, inDataCacheDictionary):
        x_in = inDataCacheDictionary['X'] # only need to perform this dictionary look-up once
        
        a = inCoeffs[0]
        b = inCoeffs[1]
        c = inCoeffs[2]

        try:
            temp = 1.0 - numpy.power(a, b * x_in + c)
            return self.extendedVersionHandler.GetAdditionalModelPredictions(temp, inCoeffs, inDataCacheDictionary, self)
        except:
            return numpy.ones(len(inDataCacheDictionary['DependentData'])) * 1.0E300

Example 32

Project: SHARPpy Source File: thermo.py
Function: theta
def theta(p, t, p2=1000.):
    '''
    Returns the potential temperature (C) of a parcel.

    Parameters
    ----------
    p : number, numpy array
        The pressure of the parcel (hPa)
    t : number, numpy array
        Temperature of the parcel (C)
    p2 : number, numpy array (default 1000.)
        Reference pressure level (hPa)

    Returns
    -------
    Potential temperature (C)

    '''
    return ((t + ZEROCNK) * np.power((p2 / p),ROCP)) - ZEROCNK

Example 33

Project: pyeq3 Source File: YieldDensity.py
Function: calculatemodelpredictions
    def CalculateModelPredictions(self, inCoeffs, inDataCacheDictionary):
        x_in = inDataCacheDictionary['X'] # only need to perform this dictionary look-up once
        
        a = inCoeffs[0]
        b = inCoeffs[1]
        c = inCoeffs[2]

        try:
            temp = 1.0 / (a + b*numpy.power(x_in, c))
            return self.extendedVersionHandler.GetAdditionalModelPredictions(temp, inCoeffs, inDataCacheDictionary, self)
        except:
            return numpy.ones(len(inDataCacheDictionary['DependentData'])) * 1.0E300

Example 34

Project: pele Source File: atom.py
    def get_lj_sigma_epsilon(self):
        """
        This calculates sigma and epsilon, based on the LJ A and B coefficients
        described above and returns them as a tuple.
        
            s = (A/B)**(1/6)
            e = B**2/4A 
        """
        sigma   = np.reciprocal(np.power(self.lj_a/self.lj_b, 6.0))
        epsilon = self.lj_b * self.lj_b / (4.0 * self.lj_a)
        return sigma, epsilon 

Example 35

Project: pyeq3 Source File: YieldDensity.py
Function: calculatemodelpredictions
    def CalculateModelPredictions(self, inCoeffs, inDataCacheDictionary):
        x_in = inDataCacheDictionary['X'] # only need to perform this dictionary look-up once
        
        a = inCoeffs[0]
        b = inCoeffs[1]
        c = inCoeffs[2]

        try:
            temp = x_in / (a + b*numpy.power(x_in, c))
            return self.extendedVersionHandler.GetAdditionalModelPredictions(temp, inCoeffs, inDataCacheDictionary, self)
        except:
            return numpy.ones(len(inDataCacheDictionary['DependentData'])) * 1.0E300

Example 36

Project: statsmodels Source File: links.py
Function: deriv2
    def deriv2(self, p):
        """
        Second derivative of the power transform

        Parameters
        ----------
        p : array-like
            Mean parameters

        Returns
        --------
        g''(p) : array
            Second derivative of the power transform of `p`

        Notes
        -----
        g''(`p`) = `power` * (`power` - 1) * `p`**(`power` - 2)
        """
        return self.power * (self.power - 1) * np.power(p, self.power - 2)

Example 37

Project: cvxpy Source File: power.py
Function: numeric
    @Elementwise.numpy_numeric
    def numeric(self, values):
        # Throw error if negative and power doesn't handle that.
        if self.p < 0 and values[0].min() <= 0:
            raise ValueError(
                "power(x, %.1f) cannot be applied to negative or zero values." % float(self.p)
            )
        elif not is_power2(self.p) and self.p != 0 and values[0].min() < 0:
            raise ValueError(
                "power(x, %.1f) cannot be applied to negative values." % float(self.p)
            )
        else:
            return np.power(values[0], float(self.p))

Example 38

Project: pyflux Source File: scores.py
    @staticmethod
    def mu_adj_score(y,loc,scale,shape,skewness):
        try:
            if (y-loc)>=0:
                return (((shape+1.0)*power(y-loc,2))/float(power(skewness,2)*shape*exp(scale) + power(y-loc,2))) - 1.0
            else:
                return (((shape+1.0)*power(y-loc,2))/float(power(skewness,-2)*shape*exp(scale) + power(y-loc,2))) - 1.0    
        except:
            return -1.0

Example 39

Project: tract_querier Source File: scalar_measures.py
def geodesic_anisotropy(evals):
    """ Taken from dipy/reconst/dti.py
    see for docuementation
    :return:
    """
    ev1, ev2, ev3 = evals

    # this is the definition in [1]_
    detD = numpy.power(ev1 * ev2 * ev3, 1 / 3.)
    if detD > 1e-9:
        log1 = numpy.log(ev1 / detD)
        log2 = numpy.log(ev2 / detD)
        log3 = numpy.log(ev3 / detD)

        ga = numpy.sqrt(log1 ** 2 + log2 ** 2 + log3 ** 2)
    else:
        ga = 0.0
    return ga

Example 40

Project: statsmodels Source File: copula.py
def copula_bv_clayton(u, v, theta):
    '''Clayton or Cook, Johnson bivariate copula
    '''
    if not theta > 0:
        raise ValueError('theta needs to be strictly positive')
    return np.power(np.power(u, -theta) + np.power(v, -theta) - 1, -theta)

Example 41

Project: kepler_orrery Source File: diverging_map.py
    def Lab2Msh(self, Lab):
        """
        Conversion of CIELAB to Msh
        """

        # unpack the Lab-array
        L, a, b = Lab.tolist()

        # calculation of M, s and h
        M = np.sqrt(np.sum(np.power(Lab, 2)))
        s = np.arccos(L/M)
        h = np.arctan2(b,a)
        return np.array([M,s,h])

Example 42

Project: pele Source File: test_spin_systems.py
Function: set_up
    def setUp(self):
        p=4
        nspins = 10
        interactions = np.ones(np.power(10,p))
        coords = np.ones(nspins)
        self.pot = MeanFieldPSpinSpherical(interactions, nspins, p)
        self.x0 = coords
        self.e0 = -210/np.power(nspins,(p-1)/2)

Example 43

Project: oq-hazardlib Source File: edwards_fah_2013a.py
    def _compute_term_1(self, C, mag):
        """
        Compute term 1
        a1 + a2.*M + a3.*M.^2 + a4.*M.^3 + a5.*M.^4 + a6.*M.^5 + a7.*M.^6
        """
        return (
            C['a1'] + C['a2'] * mag + C['a3'] *
            np.power(mag, 2) + C['a4'] * np.power(mag, 3)
            + C['a5'] * np.power(mag, 4) + C['a6'] *
            np.power(mag, 5) + C['a7'] * np.power(mag, 6)
        )

Example 44

Project: globalstar Source File: sync.py
Function: magnitude
def magnitude(a, b):

	#calculate the magnitude
	a = numpy.power(a, 2)
	b = numpy.power(b, 2)

	mag = numpy.add(a, b)

	#We really don't need to take the square root here.  It just adds overhead
	#and we may get higher resolution on the threshold without doing it. Threshold
	#just needs to be adjusted accordingly (make it larger by a square.)
	
	#mag = numpy.sqrt(mag)

	return mag

Example 45

Project: pyquante2 Source File: functionals.py
def xb88_array(rho,gam,tol=1e-6):
    # Still doesn't work
    rho = zero_low_density(rho)
    rho13 = np.power(rho,1./3.)
    x = np.zeros(rho.shape,dtype=float)
    g = np.zeros(rho.shape,dtype=float)
    dg = np.zeros(rho.shape,dtype=float)
    x[rho>tol] = np.sqrt(gam)/rho13/rho
    g[rho>tol] = b88_g(x[rho>tol])
    dg[rho>tol] = b88_dg(x[rho>tol])
    dfxdrho = (4./3.)*rho13*(g-x*dg)
    dfxdgam = 0.5*dg/np.sqrt(gam)
    fx = rho*rho13*g
    return fx,dfxdrho,dfxdgam

Example 46

Project: pele Source File: test_spin_systems.py
Function: set_up
    def setUp(self):
        p=2
        nspins = 10
        interactions = np.ones(np.power(10,p))
        coords = np.ones(nspins)
        self.pot = MeanFieldPSpinSpherical(interactions, nspins, p)
        self.x0 = coords
        self.e0 = -45

Example 47

Project: pele Source File: test_spin_systems.py
Function: set_up
    def setUp(self):
        p=5
        nspins = 10
        interactions = np.ones(np.power(10,p))
        coords = np.ones(nspins)
        self.pot = MeanFieldPSpinSpherical(interactions, nspins, p)
        self.x0 = coords
        self.e0 = -252/np.power(nspins,(p-1)/2)

Example 48

Project: imageqa-public Source File: ordinal.py
Function: forward
    def forward(self, X):
        mu = self.W[0].reshape(1, self.W.shape[1])
        pi = self.W[1].reshape(1, self.W.shape[1])
        self.Xshape = X.shape
        X = X.reshape(X.shape[0], 1)
        self.X = X
        Z = np.exp(mu * X + (pi - np.power(mu, 2) / 2))
        Y = Z / np.sum(Z, axis=-1).reshape(X.shape[0], 1)
        self.Z = Z
        self.Y = Y
        return Y

Example 49

Project: biggus Source File: test__Elementwise.py
    def test_dual_argument_operation(self):
        exponent = 2
        expected = self.masked_array ** exponent
        actual = Elementwise(self.masked_array, exponent, np.power, ma.power)
        result = actual.masked_array()
        assert_array_equal(result.mask, self.mask)
        assert_array_equal(result, expected)

Example 50

Project: pyeq2 Source File: Power.py
Function: calculatemodelpredictions
    def CalculateModelPredictions(self, inCoeffs, inDataCacheDictionary):
        x_LogX = inDataCacheDictionary['LogX'] # only need to perform this dictionary look-up once
        
        a = inCoeffs[0]

        try:
            temp = numpy.power(a, x_LogX)
            return self.extendedVersionHandler.GetAdditionalModelPredictions(temp, inCoeffs, inDataCacheDictionary, self)
        except:
            return numpy.ones(len(inDataCacheDictionary['DependentData'])) * 1.0E300
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