Python numpy.log函数代码示例

def joint_logdist(pi, alpha, sigma, tau, u):
    abs_pi = len(pi)
    n = np.sum(pi)
    tmp = abs_pi * log(alpha) + (n - 1.) * log(u) - gammaln(n) - (n - sigma * abs_pi) * log(u + tau) \
          - (alpha / sigma) * ((u + tau) ** sigma - tau ** sigma)
    tmp += np.sum(gammaln(pi - sigma) - gammaln(1. - sigma))
    return tmp

def log_diff_exp(x, axis=0):
    """ Calculates the logarithm of the diffs of e to the power of input 'x'. The method tries to avoid
        overflows by using the relationship: log(diff(exp(x))) = alpha + log(diff(exp(x-alpha))).
        
    :Parameter:
        x:    data.
             -type: float or numpy array 
          
        axis: Sums along the given axis.
             -type: int
        
    :Return:
        Logarithm of the sum of exp of x. 
       -type: float or numpy array.
        
    """
    alpha = x.max(axis) - numx.log(numx.finfo(numx.float64).max)/2.0
    if axis == 1:
        return numx.squeeze(alpha + numx.log(
                                             numx.diff(
                                                       numx.exp(x.T - alpha)
                                                       , n=1, axis=0)))
    else:
        return numx.squeeze(alpha + numx.log(
                                             numx.diff(
                                                       numx.exp(x - alpha)
                                                       , n=1, axis=0)))

def _lmvnpdffull(obs, means, covars):
    """
    Log probability for full covariance matrices.

    WARNING: In certain cases, this function will modify in-place
    some of the covariance matrices

    """
    from scipy import linalg
    import itertools
    if hasattr(linalg, 'solve_triangular'):
        # only in scipy since 0.9
        solve_triangular = linalg.solve_triangular
    else:
        # slower, but works
        solve_triangular = linalg.solve
    n_obs, n_dim = obs.shape
    nmix = len(means)
    log_prob = np.empty((n_obs, nmix))
    for c, (mu, cv) in enumerate(itertools.izip(means, covars)):
        try:
            cv_chol = linalg.cholesky(cv, lower=True)
        except linalg.LinAlgError:
            # The model is most probabily stuck in a component with too
            # few observations, we need to reinitialize this components
            cv[:] = 10 * np.eye(cv.shape[0])
            cv_chol = cv
        cv_log_det = 2 * np.sum(np.log(np.diagonal(cv_chol)))
        cv_sol = solve_triangular(cv_chol, (obs - mu).T, lower=True).T
        log_prob[:, c] = -.5 * (np.sum(cv_sol ** 2, axis=1) + \
                           n_dim * np.log(2 * np.pi) + cv_log_det)

    return log_prob

def compute_cost( X, y, theta, lam ):

    '''Compute cost for logistic regression.'''
    
    # Number of training examples
    m = y.shape[0]

    # Compute the prediction based on theta and X
    predictions = X.dot( theta )

    # Preprocessing values before sending to sigmoid function.
    # If the argument to sigmoid function >= 0, we know that the
    # sigmoid value is 1. Similarly for the negative values.
    predictions[ where( predictions >= 20 ) ] = 20
    predictions[ where( predictions <= -500 ) ] = -500
    hypothesis = sigmoid( predictions )

    hypothesis[ where( hypothesis == 1.0 ) ] = 0.99999

    # Part of the cost function without regularization
    J1 = ( -1.0 / m ) * sum( ( y * np.log( hypothesis ) ) + 
                            ( ( 1.0 - y ) * np.log( 1.0 - hypothesis ) ) ) 

    # Computing the regularization term
    J2 = lam / ( 2.0 * m ) * sum( theta[ 1:, ] * theta[ 1:, ] )
    error = hypothesis - y

    return J1 + J2

def computeCost(theta, X, y):
    theta.shape = (1, 3)
    m = y.size
    z=X.dot(theta.T)
    h = 1.0 / (1.0 + e ** (-1.0 * z))
    J = (1.0 / m) * ((-y.T.dot(log(h))) - ((1.0 - y.T).dot(log(1.0 - h))))
    return 1 * J.sum()

def klBern(x, y):
    r""" Kullback-Leibler divergence for Bernoulli distributions. https://en.wikipedia.org/wiki/Bernoulli_distribution#Kullback.E2.80.93Leibler_divergence

    .. math:: \mathrm{KL}(\mathcal{B}(x), \mathcal{B}(y)) = x \log(\frac{x}{y}) + (1-x) \log(\frac{1-x}{1-y})."""
    x = min(max(x, eps), 1 - eps)
    y = min(max(y, eps), 1 - eps)
    return x * np.log(x / y) + (1 - x) * np.log((1 - x) / (1 - y))

    def __init__(self, shape, successes, 
                 trials=None, coef=1., offset=None,
                 quadratic=None,
                 initial=None):

        smooth_atom.__init__(self,
                             shape,
                             offset=offset,
                             quadratic=quadratic,
                             initial=initial,
                             coef=coef)

        if sparse.issparse(successes):
            #Convert sparse success vector to an array
            self.successes = successes.toarray().flatten()
        else:
            self.successes = np.asarray(successes)

        if trials is None:
            if not set([0,1]).issuperset(np.unique(self.successes)):
                raise ValueError("Number of successes is not binary - must specify number of trials")
            self.trials = np.ones(self.successes.shape, np.float)
        else:
            if np.min(trials-self.successes) < 0:
                raise ValueError("Number of successes greater than number of trials")
            if np.min(self.successes) < 0:
                raise ValueError("Response coded as negative number - should be non-negative number of successes")
            self.trials = trials * 1.

        saturated = self.successes / self.trials
        deviance_terms = np.log(saturated) * self.successes + np.log(1-saturated) * (self.trials - self.successes)
        deviance_constant = -2 * coef * deviance_terms[~np.isnan(deviance_terms)].sum()

        devq = identity_quadratic(0,0,0,-deviance_constant)
        self.quadratic += devq

    def loglike(self, endog, mu, scale=1.):
        """
        Loglikelihood function for Gamma exponential family distribution.

        Parameters
        ----------
        endog : array-like
            Endogenous response variable
        mu : array-like
            Fitted mean response variable
        scale : float, optional
            The default is 1.

        Returns
        -------
        llf : float
            The value of the loglikelihood function evaluated at
            (endog,mu,scale) as defined below.

        Notes
        --------
        llf = -1/scale * sum(endog/mu + log(mu) + (scale-1)*log(endog) +\
              log(scale) + scale*gammaln(1/scale))
        where gammaln is the log gamma function.
        """
        return - 1./scale * np.sum(endog/mu + np.log(mu) + (scale - 1) *
                                   np.log(endog) + np.log(scale) + scale *
                                   special.gammaln(1./scale))

    def __init__(self, ps=None, sigma_v=0.0, redshift=0.0, **kwargs):
        if ps == None:
            from os.path import join, dirname
            #psfile = join(dirname(__file__),"data/ps_z1.5.dat")
            #psfile = join(dirname(__file__),"data/wigglez_halofit_z1.5.dat")
            psfile = join(dirname(__file__),"data/wigglez_halofit_z0.8.dat")
            print "loading matter power file: " + psfile
            redshift = 0.8

            #pk_interp = cs.LogInterpolater.fromfile(psfile)
            pwrspec_data = np.genfromtxt(psfile)

            (log_k, log_pk) = (np.log(pwrspec_data[:,0]), \
                               np.log(pwrspec_data[:,1]))

            logpk_interp = interpolate.interp1d(log_k, log_pk,
                                                bounds_error=False,
                                                fill_value=np.min(log_pk))

            pk_interp = lambda k: np.exp(logpk_interp(np.log(k)))

            kstar = 7.0
            ps = lambda k: np.exp(-0.5 * k**2 / kstar**2) * pk_interp(k)

        self._sigma_v = sigma_v

        RedshiftCorrelation.__init__(self, ps_vv=ps, redshift=redshift)

def nie_all(xi1, xi2, xc1, xc2, b, s, q, rot, ys1, ys2):

    x1, x2 = xy_rotate(xi1, xi2, xc1, xc2, rot)

    wx = np.sqrt(q * q * (x1 * x1 + s * s) + x2 * x2)

    al1 = b / np.sqrt(1 - q * q) * np.arctan(x1 * np.sqrt(1 - q * q) / (wx + s))
    al2 = b / np.sqrt(1 - q * q) * np.arctanh(x2 * np.sqrt(1 - q * q) / (wx + q * q * s))

    kappa = b / (2.0 * wx)

    hx = np.sqrt((wx + s) ** 2.0 + (1 - q * q) * x1 * x1)
    phi = x1 * al1 + x2 * al2 - b * s * np.log(hx) + b * q * s * np.log((1 + q) * s)

    Kc = 1.0
    # Kc = (1.0+zl)/c*(Dl*Ds/Dls)
    td = Kc * (0.5 * ((al1) ** 2.0 + (al2) ** 2.0) - phi)
    # td = Kc*(0.5*((x1-ys1)**2.0+(x2-ys2)**2.0)-phi)

    y1 = x1 - al1
    y2 = x2 - al2

    y1, y2 = xy_rotate(y1, y2, xc1, xc2, -rot)

    # ------------------------------------------------------------------
    demon1 = ((wx + s) ** 2 + (1.0 - q * q) * x1 * x1) * wx
    demon2 = ((wx + q * q * s) ** 2 - (1.0 - q * q) * x2 * x2) * wx
    y11 = 1 - b * (wx * (wx + s) - q * q * x1 * x1) / demon1
    y22 = 1 - b * (wx * (wx + q * q * s) - x2 * x2) / demon2
    y12 = -b * x1 * x2 / demon1
    y21 = -b * x1 * x2 * q * q / demon2

    mu = 1.0 / (y11 * y22 - y12 * y21)

    return phi, td, al1, al2, kappa, mu, y1, y2

 def _SigmoidCrossEntropyWithLogits(logits, targets):
   # logits, targets: float arrays of the same shape.
   assert logits.shape == targets.shape
   pred = 1. / (1. + np.exp(-logits))
   eps = 0.0001
   pred = np.minimum(np.maximum(pred, eps), 1 - eps)
   return -targets * np.log(pred) - (1. - targets) * np.log(1. - pred)

  def _ComputeSampledLogitsNP(self, true_w, true_b, sampled_w, sampled_b,
                              hidden_acts,
                              num_true=1,
                              true_expected=None,
                              sampled_expected=None):

    batch_size, dim = hidden_acts.shape
    true_logits = np.sum(
        hidden_acts.reshape((batch_size, 1, dim)) * true_w.reshape(
            (batch_size, num_true, dim)),
        axis=2)
    true_b = true_b.reshape((batch_size, num_true))
    true_logits += true_b
    sampled_logits = np.dot(hidden_acts, sampled_w.T) + sampled_b

    if true_expected is not None:
      true_logits -= np.log(true_expected)
    if sampled_expected is not None:
      sampled_logits -= np.log(sampled_expected[np.newaxis, :])

    out_logits = np.concatenate([true_logits, sampled_logits], axis=1)
    out_labels = np.hstack((np.ones_like(true_logits) / num_true,
                            np.zeros_like(sampled_logits)))

    return out_logits, out_labels

def test_anisotropic_power():
    for n_coeffs in [6, 15, 28, 45, 66, 91]:
        for norm_factor in [0.0005, 0.00001]:

            # Create some really simple cases:
            coeffs = np.ones((3, n_coeffs))
            max_order = calculate_max_order(coeffs.shape[-1])
            # For the case where all coeffs == 1, the ap is simply log of the
            # number of even orders up to the maximal order:
            analytic = (np.log(len(range(2, max_order + 2, 2))) -
                        np.log(norm_factor))

            answers = [analytic] * 3
            apvals = anisotropic_power(coeffs, norm_factor=norm_factor)
            assert_array_almost_equal(apvals, answers)
            # Test that this works for single voxel arrays as well:
            assert_array_almost_equal(
                anisotropic_power(coeffs[1],
                                  norm_factor=norm_factor),
                answers[1])

    # Test that even when we look at an all-zeros voxel, this
    # avoids a log-of-zero warning:
    with warnings.catch_warnings(record=True) as w:
        assert_equal(anisotropic_power(np.zeros(6)), 0)
        assert len(w) == 0

def compute_edge_weights( edge_ids, edge_probabilities, beta ):
    """
    Convert edge probabilities to energies for the multicut problem.
    
    edge_ids:
        The list of edges in the graph. shape=(N, 2)
    edge_probabilities:
        1-D, float (1.0 means edge is CUT, disconnecting the two SPs)
    beta:
        scalar (float)

    Special behavior:
        If any node has ID 0, all of it's edges will be given an
        artificially low energy, to prevent it from merging with its
        neighbors, regardless of what the edge_probabilities say.
    """
    p1 = edge_probabilities # P(Edge=CUT)
    p1 = np.clip(p1, 0.001, 0.999)
    p0 = 1.0 - p1 # P(Edge=NOT CUT)

    edge_weights = np.log(p0/p1) + np.log( (1-beta)/(beta) )

    # See note special behavior, above
    edges_touching_zero = edge_ids[:,0] == 0
    if edges_touching_zero.any():
        logger.warn("Volume contains label 0, which will be excluded from the segmentation.")
        MINIMUM_ENERGY = -1000.0
        edge_weights[edges_touching_zero] = MINIMUM_ENERGY
    
    return edge_weights

 def all_GL(self, q, maxpiv=None):
     """return (piv, f_binodal_gas, f_binodal_liquid, f_spinodal_gas, f_spinodal_liquid) at insersion works piv sampled between the critical point and maxpiv (default to 2.2*critical pressure)"""
     fc, pivc = self.critical_point(q)
     Fc = np.log(fc)
     #start sensibly above the critical point
     startp = pivc*1.1
     fm = fminbound(self.mu, fc, self.maxf(), args=(startp, q))
     fM = fminbound(lambda f: -self.pv(f, startp, q), 0, fc)
     initial_guess = np.log([0.5*fM, 0.5*(fm+self.maxf())])
     #construct the top of the GL binodal
     if maxpiv is None:
         maxpiv = startp*2
     topp = 1./np.linspace(1./startp, 1./maxpiv)
     topGL = [initial_guess]
     for piv in topp:
         topGL.append(self.binodalGL(piv, q, topGL[-1]))
     #construct the GL binodal between the starting piv and the critical point
     botp = np.linspace(startp, pivc)[:-1]
     botGL = [initial_guess]
     for piv in botp:
         botGL.append(self.binodalGL(piv, q, botGL[-1]))
     #join the two results and convert back from log
     binodal = np.vstack((
         [[pivc, fc, fc]],
         np.column_stack((botp, np.exp(botGL[1:])))[::-1],
         np.column_stack((topp, np.exp(topGL[1:])))[1:]
         ))
     #spinodal at the same pivs
     spinodal = self.spinodalGL(q, binodal[:,0])
     #join everything
     return np.column_stack((binodal, spinodal[:,1:]))