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📄 multilinear.c

📁 该文件为c++的数学函数库!是一个非常有用的编程工具.它含有各种数学函数,为科学计算、工程应用等程序编写提供方便!
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/* multifit/multilinear.c *  * Copyright (C) 2000 Brian Gough *  * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or (at * your option) any later version. *  * This program is distributed in the hope that it will be useful, but * WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU * General Public License for more details. *  * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */#include <config.h>#include <gsl/gsl_errno.h>#include <gsl/gsl_multifit.h>#include <gsl/gsl_blas.h>#include <gsl/gsl_linalg.h>/* Fit * *  y = X c * *  where X is an M x N matrix of M observations for N variables. * */intgsl_multifit_linear (const gsl_matrix * X,                     const gsl_vector * y,                     gsl_vector * c,                     gsl_matrix * cov,                     double *chisq, gsl_multifit_linear_workspace * work){  if (X->size1 != y->size)    {      GSL_ERROR        ("number of observations in y does not match rows of matrix X",         GSL_EBADLEN);    }  else if (X->size2 != c->size)    {      GSL_ERROR ("number of parameters c does not match columns of matrix X",                 GSL_EBADLEN);    }  else if (cov->size1 != cov->size2)    {      GSL_ERROR ("covariance matrix is not square", GSL_ENOTSQR);    }  else if (c->size != cov->size1)    {      GSL_ERROR        ("number of parameters does not match size of covariance matrix",         GSL_EBADLEN);    }  else if (X->size1 != work->n || X->size2 != work->p)    {      GSL_ERROR        ("size of workspace does not match size of observation matrix",         GSL_EBADLEN);    }  else    {      const size_t n = X->size1;      const size_t p = X->size2;      size_t i, j;      gsl_matrix *A = work->A;      gsl_matrix *Q = work->Q;      gsl_matrix *QSI = work->QSI;      gsl_vector *S = work->S;      gsl_vector *xt = work->xt;      gsl_vector *D = work->D;      /* Copy X to workspace,  A <= X */      gsl_matrix_memcpy (A, X);      /* Balance the columns of the matrix A */      gsl_linalg_balance_columns (A, D);      /* Decompose A into U S Q^T */      gsl_linalg_SV_decomp_mod (A, QSI, Q, S, xt);      /* Solve y = A c for c */      gsl_blas_dgemv (CblasTrans, 1.0, A, y, 0.0, xt);      /* Scale the matrix Q,  Q' = Q S^-1 */      gsl_matrix_memcpy (QSI, Q);      for (j = 0; j < p; j++)        {          gsl_vector_view column = gsl_matrix_column (QSI, j);          double alpha = gsl_vector_get (S, j);          if (alpha != 0)            alpha = 1.0 / alpha;          gsl_vector_scale (&column.vector, alpha);        }      gsl_vector_set_zero (c);      gsl_blas_dgemv (CblasNoTrans, 1.0, QSI, xt, 0.0, c);      /* Unscale the balancing factors */      gsl_vector_div (c, D);      /* Compute chisq, from residual r = y - X c */      {        double s2 = 0, r2 = 0;        for (i = 0; i < n; i++)          {            double yi = gsl_vector_get (y, i);            gsl_vector_const_view row = gsl_matrix_const_row (X, i);            double y_est, ri;            gsl_blas_ddot (&row.vector, c, &y_est);            ri = yi - y_est;            r2 += ri * ri;          }        s2 = r2 / (n - p);        *chisq = r2;        /* Form variance-covariance matrix cov = s2 * (Q S^-1) (Q S^-1)^T */        for (i = 0; i < p; i++)          {            gsl_vector_view row_i = gsl_matrix_row (QSI, i);            double d_i = gsl_vector_get (D, i);            for (j = i; j < p; j++)              {                gsl_vector_view row_j = gsl_matrix_row (QSI, j);                double d_j = gsl_vector_get (D, j);                double s;                gsl_blas_ddot (&row_i.vector, &row_j.vector, &s);                gsl_matrix_set (cov, i, j, s * s2 / (d_i * d_j));                gsl_matrix_set (cov, j, i, s * s2 / (d_i * d_j));              }          }      }      return GSL_SUCCESS;    }}intgsl_multifit_wlinear (const gsl_matrix * X,                      const gsl_vector * w,                      const gsl_vector * y,                      gsl_vector * c,                      gsl_matrix * cov,                      double *chisq, gsl_multifit_linear_workspace * work){  if (X->size1 != y->size)    {      GSL_ERROR        ("number of observations in y does not match rows of matrix X",         GSL_EBADLEN);    }  else if (X->size2 != c->size)    {      GSL_ERROR ("number of parameters c does not match columns of matrix X",                 GSL_EBADLEN);    }  else if (w->size != y->size)    {      GSL_ERROR ("number of weights does not match number of observations",                 GSL_EBADLEN);    }  else if (cov->size1 != cov->size2)    {      GSL_ERROR ("covariance matrix is not square", GSL_ENOTSQR);    }  else if (c->size != cov->size1)    {      GSL_ERROR        ("number of parameters does not match size of covariance matrix",         GSL_EBADLEN);    }  else if (X->size1 != work->n || X->size2 != work->p)    {      GSL_ERROR        ("size of workspace does not match size of observation matrix",         GSL_EBADLEN);    }  else    {      const size_t n = X->size1;      const size_t p = X->size2;      size_t i, j;      gsl_matrix *A = work->A;      gsl_matrix *Q = work->Q;      gsl_matrix *QSI = work->QSI;      gsl_vector *S = work->S;      gsl_vector *t = work->t;      gsl_vector *xt = work->xt;      gsl_vector *D = work->D;      /* Scale X,  A = sqrt(w) X */      gsl_matrix_memcpy (A, X);      for (i = 0; i < n; i++)        {          double wi = gsl_vector_get (w, i);          if (wi < 0)            wi = 0;          {            gsl_vector_view row = gsl_matrix_row (A, i);            gsl_vector_scale (&row.vector, sqrt (wi));          }        }      /* Balance the columns of the matrix A */      gsl_linalg_balance_columns (A, D);      /* Decompose A into U S Q^T */      gsl_linalg_SV_decomp_mod (A, QSI, Q, S, xt);      /* Solve sqrt(w) y = A c for c, by first computing t = sqrt(w) y */      for (i = 0; i < n; i++)        {          double wi = gsl_vector_get (w, i);          double yi = gsl_vector_get (y, i);          if (wi < 0)            wi = 0;          gsl_vector_set (t, i, sqrt (wi) * yi);        }      gsl_blas_dgemv (CblasTrans, 1.0, A, t, 0.0, xt);      /* Scale the matrix Q,  Q' = Q S^-1 */      gsl_matrix_memcpy (QSI, Q);      for (j = 0; j < p; j++)        {          gsl_vector_view column = gsl_matrix_column (QSI, j);          double alpha = gsl_vector_get (S, j);          if (alpha != 0)            alpha = 1.0 / alpha;          gsl_vector_scale (&column.vector, alpha);        }      gsl_vector_set_zero (c);      /* Solution */      gsl_blas_dgemv (CblasNoTrans, 1.0, QSI, xt, 0.0, c);      /* Unscale the balancing factors */      gsl_vector_div (c, D);      /* Form covariance matrix cov = (Q S^-1) (Q S^-1)^T */      for (i = 0; i < p; i++)        {          gsl_vector_view row_i = gsl_matrix_row (QSI, i);          double d_i = gsl_vector_get (D, i);          for (j = i; j < p; j++)            {              gsl_vector_view row_j = gsl_matrix_row (QSI, j);              double d_j = gsl_vector_get (D, j);              double s;              gsl_blas_ddot (&row_i.vector, &row_j.vector, &s);              gsl_matrix_set (cov, i, j, s / (d_i * d_j));              gsl_matrix_set (cov, j, i, s / (d_i * d_j));            }        }      /* Compute chisq, from residual r = y - X c */      {        double r2 = 0;        for (i = 0; i < n; i++)          {            double yi = gsl_vector_get (y, i);            double wi = gsl_vector_get (w, i);            gsl_vector_const_view row = gsl_matrix_const_row (X, i);            double y_est, ri;            gsl_blas_ddot (&row.vector, c, &y_est);            ri = yi - y_est;            r2 += wi * ri * ri;          }        *chisq = r2;      }      return GSL_SUCCESS;    }}

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