Python linalg.lstsq函数代码示例

def partial_corr(C):
    """
    Returns the sample linear partial correlation coefficients between pairs of variables in C, controlling
    for the remaining variables in C.
    Parameters
    ----------
    C : array-like, shape (n, p)
        Array with the different variables. Each column of C is taken as a variable
    Returns
    -------
    P : array-like, shape (p, p)
        P[i, j] contains the partial correlation of C[:, i] and C[:, j] controlling
        for the remaining variables in C.
    """

    C = np.asarray(C)
    p = C.shape[1]
    P_corr = np.zeros((p, p), dtype=np.float)
    for i in range(p):
        P_corr[i, i] = 1
        for j in range(i+1, p):
            idx = np.ones(p, dtype=np.bool)
            idx[i] = False
            idx[j] = False
            beta_i = linalg.lstsq(C[:, idx], C[:, j])[0]
            beta_j = linalg.lstsq(C[:, idx], C[:, i])[0]

            res_j = C[:, j] - C[:, idx].dot( beta_i)
            res_i = C[:, i] - C[:, idx].dot(beta_j)

            corr = stats.pearsonr(res_i, res_j)[0]
            P_corr[i, j] = corr
            P_corr[j, i] = corr

    return P_corr

def bilinear_least_squares(X, y, b0=None, n_iter=10, fit_intercept=True):
    """assumes X.shape = n_samples, n_matrices, h, wi
       and does linear regression as a sum of rank 1 matrices"""

    if X.ndim == 3:
        X = X[:, np.newaxis]
    n_samples, n_matrices, n_feat_a, n_feat_b = X.shape
    if b0 is None:
        b0 = np.ones((n_matrices, n_feat_b)) / n_feat_b
    b = b0.copy()

    if fit_intercept:
        X_mean, y_mean = X.mean(0), y.mean()
        X = X - X_mean
        y = y - y_mean

    for i in range(n_iter):
        a_estimation_matrix = np.einsum(
                                 "ijkl, jl -> ijk", X, b).reshape(n_samples, -1)
        a = lstsq(a_estimation_matrix, y)[0].reshape(n_matrices, n_feat_a)
        b_estimation_matrix = np.einsum(
                                 "ijkl, jk -> ijl", X, a).reshape(n_samples, -1)
        b = lstsq(b_estimation_matrix, y)[0].reshape(n_matrices, n_feat_b)


    if fit_intercept:
        intercept = y_mean - np.einsum("jkl, jk, jl", X_mean, a, b)
        return a, b, intercept

    return a, b

def pcor(X,Y,Z):
    """
    computes the correlation amtrix of X and Y conditioning on Z
    """
    if X.ndim==1: X = X[:,SP.newaxis]
    if Y.ndim==1: Y = Y[:,SP.newaxis]
    
    if Z is None: return STATS.pearsonr(X,Y)

    if Z.ndim==1: Z = Z[:,SP.newaxis]
    nSamples = X.shape[0]
    betaX, _, _, _ = LA.lstsq(Z,X)
    betaY, _, _, _ = LA.lstsq(Z,Y)
    Xres = X - SP.dot(Z,betaX)
    Yres = Y - SP.dot(Z,betaY)
    corr_cond = SP.corrcoef(Xres[:,0],Yres[:,0])[0,1]
    dz = Z.shape[1]  # dimension of conditioning variable
    df = max(nSamples - dz - 2,0)  # degrees of freedom

    with warnings.catch_warnings():
        warnings.filterwarnings("ignore")
        tstat = corr_cond / SP.sqrt(1.0 - corr_cond ** 2)  # calculate t statistic
        
    tstat = math.sqrt(df) * tstat
    pv_cond = 2 * t.cdf(-abs(tstat), df, loc=0, scale=1)  # calculate p value
    return corr_cond,pv_cond

def pcorParallel(X,Z,Y=None):
    """
    computes the correlation matrix between X and Y conditioning on Z
    """
    if Y is None: return pcorParallelSym(X,Z)
    if Z is None: return corrParallel(X,Y)

 
    if Z.ndim==1: Z = Z[SP.newaxis,:]

    X = X.T
    Y = Y.T
    Z = Z.T
    
    beta,_,_,_ = LA.lstsq(Z,Y)
    Yres = Y - SP.dot(Z,beta)

    beta,_,_,_ = LA.lstsq(Z,X)
    Xres = X - SP.dot(Z,beta)

    
    nSamples = Z.shape[0]
    nCovs = Z.shape[1]
    df = max(nSamples  - 2 - nCovs,0)
    
    return corrParallel(Xres.T,Yres.T,df=df)

    def process(self, data_in, obs_vec):
        """
        Generate function network model.

        :param data: Training data matrix :math:`\mathcal{X}\in\mathbb{R}^{d\\times n}`
        :type data: numpy array
        :param obs_vec: Observation vector :math:`y\in\mathbb{R}^{1 \\times n}`
        :type obs_vec: numpy array
        :return: none
        :rtype:  none
        """
        # check consistency of data
        obs_num = obs_vec.shape[1]
        data_num = data_in.shape[1]

        if obs_num != data_num:
            raise Exception("Number of samples for data and observations must be the same")
        else:
            # initialize variables
            self.data = data_in
            self.data_dim = data_in.shape[0]
            nsamp = data_num

            # peel off parameters
            ki = self.k_type
            bandwidth = ki.params[0]

            # compute regularized kernel matrix
            kmat = kernel(self.data, self.data, self.k_type) + (pow(self.noise,2))*eye(nsamp)

            # perform Cholesky factorization, and compute mean vector (for stable inverse computations)
            self.lmat = cholesky(kmat).transpose()
            self.mean_vec = lstsq(self.lmat, obs_vec)
            self.mean_vec = lstsq(self.lmat.transpose(), self.mean_vec)

def pcorr(C,k):
    val=list(C.columns.values)
    C[u'ones']=np.ones(C.shape[0])
    C = np.asarray(C)
    p = C.shape[1]
    P_corr = np.zeros((p-1, p-1), dtype=np.float)
    idx = np.zeros(p, dtype=np.bool)
    for kk in k:
      idx[kk] = True
    idx[p-1] = True
    for i in range(p-1):
        P_corr[i, i] = 1
        for j in range(i+1, p-1):
            beta_i = linalg.lstsq(C[:, idx], C[:, i])[0]
            beta_j = linalg.lstsq(C[:, idx], C[:, j])[0]

            res_j = C[:, j] - C[:, idx].dot(beta_j)
            res_i = C[:, i] - C[:, idx].dot(beta_i)
            
            corr = stats.pearsonr(res_i, res_j)[0]
            P_corr[i, j] = corr
            P_corr[j, i] = corr
    
    p=pd.DataFrame(P_corr, index=val, columns=val)
    return p

 def divide(self,x,mode):
     if mode == 1:
         y = linalg.lstsq(self.diag,x)
     else:
         y = linalg.lstsq(self.diag.conj().T,x)
     
     return y[0]

def solveQ3(trainData, testData, columnLabel):
    target = trainingData[:,dataMEDVCol:dataMEDVCol+1]
    targetTest = testingData[:,dataMEDVCol:dataMEDVCol+1]
    Ones = np.ones((len(target),1))

    # Fitting the parameters: theta = (X'*X)^-1*X'*y
    Xtrain = np.hstack((Ones, trainData.reshape(len(Ones),1)))
    mTheta = lstsq(Xtrain, target)[0]
    target_pred = dot(Xtrain, mTheta)
    t = target-target_pred
    msePred = sum((target-target_pred)**2)/len(target)
    meanTarget = sum(target)/len(target)
    varianceTarget = sum((target-meanTarget)**2)/len(target)
    FVU = msePred/varianceTarget

    Xtest = np.hstack((Ones, testData.reshape(len(Ones),1)))
    mThetaTest = lstsq(Xtest, targetTest)[0]
    # use theta from training set, not from testing set
    target_pred_test = dot(Xtest, mTheta)

    msePredTest = sum((targetTest-target_pred_test)**2)/len(targetTest)
    meanTargetTest = sum(targetTest)/len(targetTest)
    varianceTargetTest = sum((targetTest-meanTargetTest)**2)/len(targetTest)
    FVUTest = msePredTest/varianceTargetTest

    print '###',columnLabel,'###'
    print 'MSE training set:', msePred
    print 'MSE testing set:', msePredTest
    print 'R2 of testing set against theta from training set:', 1 - FVUTest,'\n'

 def divide(self,b,mode):
     if mode == 1:
         y = sci.lstsq(self.matrix,b)
     else:
         y = sci.lstsq(self.matrix.conj().T,b)
     
     return y[0]

def backgroundCorrectPSFWF(d):
    import numpy as np
    from scipy import linalg
    
    zf = d.shape[2]/2
        
    #subtract a linear background in x
    Ax = np.vstack([np.ones(d.shape[0]), np.arange(d.shape[0])]).T        
    bgxf = (d[0,:,zf] + d[-1,:,zf])/2
    gx = linalg.lstsq(Ax, bgxf)[0]
    
    d = d - np.dot(Ax, gx)[:,None,None]
    
    #do the same in y
    Ay = np.vstack([np.ones(d.shape[1]), np.arange(d.shape[1])]).T        
    bgyf = (d[:,0,zf] + d[:,-1,zf])/2
    gy = linalg.lstsq(Ay, bgyf)[0]
    
    d = d - np.dot(Ay, gy)[None, :,None]
    
    
    #estimate background on central slice as mean of rim pixels
    #bgr = (d[0,:,zf].mean() + d[-1,:,zf].mean() + d[:,0,zf].mean() + d[:,-1,zf].mean())/4
    
    #sum over all pixels (and hence mean) should be preserved over z (for widefield psf)
    dm = d.mean(1).mean(0)
    
    bg = dm - dm[zf]
    
    return np.maximum(d - bg[None, None, :], 0) +  1e-5

 def trialFunFit_constrained(self, s, arr, alphas, pairs, zerostart=False):
     deg = len(alphas)
     carr = np.concatenate((arr.real, arr.imag))
     # construct matrix for extended fitting problem
     A = np.concatenate((1. / (s[:,None] + alphas[None,:]), \
                             arr[:,None] / (s[:,None] + alphas[None,:])), axis=1)
     # implement the constraint
     pairsnew = np.concatenate((pairs, pairs))
     for i, p in enumerate(pairsnew):
         if p:
             x1 = A[:,i] + A[:,i+1]
             x2 = 1j * (A[:,i] - A[:,i+1])
             A[:,i] = x1
             A[:,i+1] = x2
     A = np.concatenate((A.real, A.imag), axis=0)
     # find auxiliary residues
     c = la.lstsq(A, carr)[0][-len(alphas):]
     # find zeros of fitted auxiliary function
     a = np.diag(alphas)
     b = np.ones(deg)
     # implement similarity transform
     for i, p in enumerate(pairs):
         if p:
             a[i:i+2, i:i+2] = np.array([[alphas[i].real, alphas[i].imag], \
                                         [-alphas[i].imag, alphas[i].real]])
             b[i:i+2] = np.array([2,0])
     H = a.real - np.dot(b[:,None], c[None,:])
     alphanew = np.linalg.eig(H)[0]
     inds = np.argsort(alphanew)
     alphanew = alphanew[inds]
     # indicates where pairs of complex conjugate poles occur
     auxarr = np.abs((np.abs(alphanew[:-1]) - np.abs(alphanew[1:])) / np.abs(alphanew[:-1]))
     auxarr2 = np.abs(alphas.imag) > 1e-15
     pairs = np.logical_and(np.concatenate((auxarr < 1e-15, np.zeros(1, dtype=bool))), auxarr2)
     # find residues
     Anew = 1. / (s[:,None] + alphanew[None,:])
     for i, p in enumerate(pairs):
         if p:
             x1 = Anew[:,i] + Anew[:,i+1]
             x2 = 1j * (Anew[:,i] - Anew[:,i+1])
             Anew[:,i] = x1
             Anew[:,i+1] = x2
     Anew = np.concatenate((Anew.real, Anew.imag), axis=0)
     if zerostart:
         # enforce K(t=0)=0 constraint
         row1 = np.ones(2*deg)
         for i, p in enumerate(pairs):
             if p:
                 row1[i+1] = 0
         Anew = np.concatenate((np.ones((1, deg), dtype=complex), Anew), axis=0)
         carr = np.concatenate((np.zeros(1, dtype=complex), carr))
     cnew = la.lstsq(Anew, carr)[0]
     cnew = np.array(cnew, dtype=complex)
     # recast cnew to complex values
     for i, p in enumerate(pairs):
         if p:
             cnew[i:i+2] = np.array([cnew[i] + 1j * cnew[i+1], cnew[i] - 1j * cnew[i+1]])
     
     return alphanew, cnew, pairs

def partial_corr(C):
    """
    Partial Correlation in Python (clone of Matlab's partialcorr)
    from https://gist.github.com/fabianp/9396204419c7b638d38f

    This uses the linear regression approach to compute the partial
    correlation (might be slow for a huge number of variables). The
    algorithm is detailed here:

        http://en.wikipedia.org/wiki/Partial_correlation#Using_linear_regression

    Taking X and Y two variables of interest and Z the matrix with all the variable minus {X, Y},
    the algorithm can be summarized as

        1) perform a normal linear least-squares regression with X as the target and Z as the predictor
        2) calculate the residuals in Step #1
        3) perform a normal linear least-squares regression with Y as the target and Z as the predictor
        4) calculate the residuals in Step #3
        5) calculate the correlation coefficient between the residuals from Steps #2 and #4;

    The result is the partial correlation between X and Y while controlling for the effect of Z

    Returns the sample linear partial correlation coefficients between pairs of variables in C, controlling
    for the remaining variables in C.


    Parameters
    ----------
    C : array-like, shape (n, p)
        Array with the different variables. Each column of C is taken as a variable


    Returns
    -------
    P : array-like, shape (p, p)
        P[i, j] contains the partial correlation of C[:, i] and C[:, j] controlling
        for the remaining variables in C.
    """

    C = np.asarray(C)
    p = C.shape[1]
    P_corr = np.zeros((p, p), dtype=np.float)
    for i in range(p):
        P_corr[i, i] = 1
        for j in range(i + 1, p):
            idx = np.ones(p, dtype=np.bool)
            idx[i] = False
            idx[j] = False
            beta_i = linalg.lstsq(C[:, idx], C[:, j])[0]
            beta_j = linalg.lstsq(C[:, idx], C[:, i])[0]

            res_j = C[:, j] - C[:, idx].dot(beta_i)
            res_i = C[:, i] - C[:, idx].dot(beta_j)

            corr = stats.pearsonr(res_i, res_j)[0]
            P_corr[i, j] = corr
            P_corr[j, i] = corr

    return P_corr

 def fit_values(self, s, x, damp=0.0):
     Phi = complete_polynomial(s.T, self.d).T
     self.Phi = Phi
     if damp == 0.0:
         self.coefs = np.ascontiguousarray(lstsq(Phi, x)[0])
     else:
         new_coefs = np.ascontiguousarray(lstsq(Phi, x)[0])
         self.coefs = (1 - damp) * new_coefs + damp * self.coefs

 def time_lstsq(self, dtype, size, lapack_driver):
     if lapack_driver == 'numpy':
         np.linalg.lstsq(self.A, self.b,
                         rcond=np.finfo(self.A.dtype).eps * 100)
     else:
         sl.lstsq(self.A, self.b, cond=None, overwrite_a=False,
                  overwrite_b=False, check_finite=False,
                  lapack_driver=lapack_driver)

def _unscented_correct(cross_sigma, mu_pred, sigma2_pred, obs_mu_pred, obs_sigma2_pred, z):
    """Correct predicted state estimates with an observation

    Parameters
    ----------
    cross_sigma : [n_dim_state, n_dim_obs] array
        cross-covariance between the state at time t given all observations
        from timesteps [0, t-1] and the observation at time t
    mu_pred : [n_dim_state] array
        mean of state at time t given observations from timesteps [0, t-1]
    sigma2_pred : [n_dim_state, n_dim_state] array
        square root of covariance of state at time t given observations from
        timesteps [0, t-1]
    obs_mu_pred : [n_dim_obs] array
        mean of observation at time t given observations from times [0, t-1]
    obs_sigma2_pred : [n_dim_obs] array
        square root of covariance of observation at time t given observations
        from times [0, t-1]
    z : [n_dim_obs] array
        observation at time t

    Returns
    -------
    mu_filt : [n_dim_state] array
        mean of state at time t given observations from time steps [0, t]
    sigma2_filt : [n_dim_state, n_dim_state] array
        square root of covariance of state at time t given observations from
        time steps [0, t]
    """
    n_dim_state = len(mu_pred)
    n_dim_obs = len(obs_mu_pred)

    if not np.any(ma.getmask(z)):
        ##############################################
        # Same as this, but more stable (supposedly) #
        ##############################################
        # K = cross_sigma.dot(
        #     linalg.pinv(
        #         obs_sigma2_pred.T.dot(obs_sigma2_pred)
        #     )
        # )
        ##############################################

        # equivalent to this MATLAB code
        # K = (cross_sigma / obs_sigma2_pred.T) / obs_sigma2_pred
        K = linalg.lstsq(obs_sigma2_pred, cross_sigma.T)[0]
        K = linalg.lstsq(obs_sigma2_pred.T, K)[0]
        K = K.T

        # correct mu, sigma
        mu_filt = mu_pred + K.dot(z - obs_mu_pred)
        U = K.dot(obs_sigma2_pred)
        sigma2_filt = cholupdate(sigma2_pred, U.T, -1.0)
    else:
        # no corrections to be made
        mu_filt = mu_pred
        sigma2_filt = sigma2_pred
    return (mu_filt, sigma2_filt)