sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 17k | content stringlengths 1 200k |
|---|---|---|---|---|
ac8f0ce82389dc23891edd0f07e848a5a9496472c83edecee2cb68e044fe6eeb | C | 2,275 | 64 | /* ---------------------------------------------------------- */
/* mexFunction: sparse_inp_native */
/* */
/* inner-product and apply sparse selection */
/* for array simplicity, we follow LMaFit to use transpose U... |
ac14a1486103f61a61adee3aae4a9b0a03cf7046a84daecc13039849904352f2 | C | 2,313 | 70 | /* ---------------------------------------------------------- */
/* mexFunction: segSubg_loss */
/* */
/* compute subgradient (without X') for each provided segment */
/* ... |
c08fa41b19d6c3ea1a2e181523c3ec44ec218ff82eaab793a9e25d219da11499 | C | 2,326 | 119 | #include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include "mex.h"
#ifdef MX_API_VER
#if MX_API_VER < 0x07030000
typedef int mwIndex;
#endif
#endif
void exit_with_help()
{
mexPrintf(
"Usage: libsvmwrite('filename', label_vector, instance_matrix);\n"
);
}
static void fake_answer(int nlhs, mxArray *plhs[])... |
93c64361a1ed6f8534292eccd0b82c89a96f4265ce316d92019b30ea5d150925 | C | 2,467 | 145 | #include <stdio.h>
#include <ctype.h>
#include "mex.h"
#if MX_API_VER < 0x07030000
typedef int mwIndex;
#endif
void exit_with_help()
{
mexPrintf(
"Usage: [label_vector, instance_matrix] = read_sparse(fname);\n"
);
}
static void fake_answer(mxArray *plhs[])
{
plhs[0] = mxCreateDoubleMatrix(0, 0, mxREAL);
plhs[... |
6767e66ff44e39d036c51b6c9d138c8ce818cd5f06380887b15ec50f553500a7 | C | 2,578 | 106 | /* see solve_triu for compilation instructions.
*/
#include "mex.h"
#include <string.h>
#ifdef mxCreateScalarDouble
#define BLAS64
#endif
#ifdef BLAS64
#include "blas.h"
#else
#ifdef UNDERSCORE_LAPACK_CALL
/* Thanks to Ruben Martinez-Cantin */
extern int dtrsm_(char *side, char *uplo, char *transa, char *diag,
... |
8c7189ea77c040ffb6a9aa6f838e2e9953e079bcbd41c27277c4684eb2007081 | C | 2,666 | 87 | /* takes the dot product between a length of 3 deformation vector in a
4D volume and the surface normals of a polyhedral object.
Author: Jason Lerch <jason@sickkids.ca>
*/
#define HAVE_MINC2 1
#include <volume_io.h>
#include <bicpl.h>
#include <stdio.h>
int main(int argc, char *argv[]) {
VIO_Volume ... |
a4eb0707153243092588170bd1825e1733b17eefc2f00fa1c28336a3db707487 | C | 2,780 | 124 | /*
* =============================================================
* computegr.c
*
* input: x,i,j,d
* x : matrix DxN
* i,j : indices of neighbors 1xC
* d : distances 1xC
*
* output:
* dx : matrix DxN
* =============================================================
*/
/* $Revision: 1.1... |
45d832b598f2f83465341b157771c8519a4a6bc53b4e8953df1ea9721374a95d | C | 2,882 | 104 | /* AutoCorr
* auto correlations
* MEX file
*
* batta 1999, lipa 2000
*
* input: t1: a time series to auto correlate
* (assumed to be sorted)
* binsize: the binsize for the auto corr histogram
* nbins: the number of bins
* NOTE: ASSUMES t1, binsize, nbins in SAME units
* out... |
58529d5a27c3b6b1cdabd60dbc417cefa95ee4aeb1c0b500a6b022bb2b54e577 | C | 3,045 | 90 | #include <stdio.h>
#include <stdlib.h>
#include <sys/time.h>
#include "MIToolbox/ArrayOperations.h"
#include "MIToolbox/Entropy.h"
#include "MIToolbox/MutualInformation.h"
int main(int argc, char *argv[])
{
int i;
double length, miTarget, entropyTarget, cmiTarget;
double firstEntropy, secondEntropy, thirdEntrop... |
529c129be172c91b58a574a8f16939f5aaf79772d0010f7f137f717fe5a81e80 | C | 3,108 | 98 | /*******************************************************************************
** RenyiEntropy.c
** Part of the mutual information toolbox
**
** Contains functions to calculate the Renyi alpha entropy of a single variable
** H_\alpha(X), the Renyi joint entropy of two variables H_\alpha(X,Y), and the
** conditional... |
9bdb9dad243624653d568674533360157068178bb23d3c0410f3ce320b95579c | C | 3,239 | 132 | /* CrossCorr
* cross correlations
* MEX file
*
* batta 1999
*
* input: t1, t2: two time series to cross correlate
* (assumed to be sorted)
* binsize: the binsize for the cross corr histogram
* nbins: the number of bins
* NOTE: ASSUMES t1, t2, binsize, nbins in SAME units
*... |
2b7754970d67857ec731fc6f6cadbbf13e3fe6db580f1a5d0dcb7cd05cd13927 | C | 3,520 | 107 | /* Performs a superparamagnetic-inspired hierarchical clustering algorithm
* on multidimensional samples.
*
* USAGE
*
* [C, P] = spc_mex(D, MCS)
*
* COMPILATION
*
* mex spc_mex.c SPC.c MergeSort.c L.c FAIRSPLIT.c ALGRAPH.c
*
* INPUT
* D - Ndimensions-by-Npoints array of feature data
* MCS - mi... |
c28e639b88aa2cab71c2e2b01f6fb39f7fb78960f2033c8293db7bbdb7fda162 | C | 3,609 | 96 | /*******************************************************************************
** RenyiMutualInformation.c
** Part of the mutual information toolbox
**
** Contains functions to calculate the Renyi mutual information of
** two variables X and Y, I_\alpha(X;Y), using the Renyi alpha divergence and
** the joint entrop... |
8cfb1a4ac19efd45ef950579956eaff90d37e5148887f5dd774c417d8ec1f526 | C | 3,744 | 190 | #include <stdlib.h>
#include <string.h>
#include "linear.h"
#include "mex.h"
#ifdef MX_API_VER
#if MX_API_VER < 0x07030000
typedef int mwIndex;
#endif
#endif
#define Malloc(type,n) (type *)malloc((n)*sizeof(type))
#define NUM_OF_RETURN_FIELD 7
static const char *field_names[] = {
"Parameters",
"nr_class",
"nr_f... |
0e100340514fb5e342c0a84f1cfda0586cdaadc3ce10b90c91671310addf2804 | C | 3,746 | 111 | #include "mex.h"
#include <math.h>
void mexFunction(
int nlhs, mxArray *plhs[],
int nrhs, const mxArray *prhs[]
)
{
/* Declare variables. */
int *irs, *jcs, *k_ind, i, j, k, l, m, n, p, cc, ck, nzmax, min_ind, *ind;
double *d, *k_d, *pr, *sr, dist, tmp, min_d;
mxArray ... |
b303730464f028b7acdcc3a770fef31a4883ef68c92e7061399e6656ada8e3cb | C | 3,823 | 200 | #include <stdio.h>
#include <string.h>
#include <stdlib.h>
#include <ctype.h>
#include <errno.h>
#include "mex.h"
#if MX_API_VER < 0x07030000
typedef int mwIndex;
#endif
#define max(x,y) (((x)>(y))?(x):(y))
#define min(x,y) (((x)<(y))?(x):(y))
void exit_with_help()
{
mexPrintf(
"Usage: [label_vector, instance_mat... |
78dacf5ed610c5a65a5c22892b8096deff3edbfafe829cef983aca60f3cce286 | C | 3,878 | 132 | /****
* Needs two inputs: a structure segmentation map and
* jacobians. Will then estimate the volume of each structure
* by multiplying each voxel by the voxel's volume times the
* jacobian
****/
#include <minc2.h>
#include <stdio.h>
#include <stdlib.h>
#include <math.h>
int main(int argc, char **argv) {
mih... |
b64c7770f1eb9e7776c7a5c4f8820fb46f5db75124b8bd9470fc627449a2fde4 | C | 3,918 | 96 | /*******************************************************************************
** MutualInformation.c
** Part of the mutual information toolbox
**
** Contains functions to calculate the mutual information of
** two variables X and Y, I(X;Y), to calculate the joint mutual information
** of two variables X & Z on the ... |
a7ad94c15539206a89be12e69686025b3c9838d925335729f5014f70641e6b7f | C | 3,939 | 104 | /*******************************************************************************
** RenyiMutualInformation.c
** Part of the mutual information toolbox
**
** Contains functions to calculate the Renyi mutual information of
** two variables X and Y, I_\alpha(X;Y), using the Renyi alpha divergence and
** the joint entrop... |
cecccaf148941a3a7782d6469d0d712fa531d8d70cc0f1ba49c1f9b41501d133 | C | 3,951 | 130 | /*******************************************************************************
** Entropy.c
** Part of the mutual information toolbox
**
** Contains functions to calculate the entropy of a single variable H(X),
** the joint entropy of two variables H(X,Y), and the conditional entropy
** H(X|Y)
**
** Author: Adam Po... |
6a42f100b8c2045b9052bbcb01be1dadf49164b81785b29b03e266f7be9cf8a8 | C | 4,060 | 212 | #include <stdio.h>
#include <string.h>
#include <stdlib.h>
#include <ctype.h>
#include <errno.h>
#include "mex.h"
#ifdef MX_API_VER
#if MX_API_VER < 0x07030000
typedef int mwIndex;
#endif
#endif
#ifndef max
#define max(x,y) (((x)>(y))?(x):(y))
#endif
#ifndef min
#define min(x,y) (((x)<(y))?(x):(y))
#endif
void exit_... |
1eebe3c25259ec6b5f81ae341bdd6be42077ba942414d8cab8eb5ec1f0f42eb8 | C | 4,062 | 186 | #include <stdio.h>
#include <ctype.h>
#include <stdlib.h>
#include <string.h>
#include "svm.h"
struct svm_node *x;
int max_nr_attr = 64;
struct svm_model* model;
int predict_probability=0;
void predict(FILE *input, FILE *output)
{
int correct = 0;
int total = 0;
double error = 0;
double sumv = 0, sumy = 0, sumvv... |
df80dd7dfe1c8b9806741d3502141403354b36d494798f3848d5f79965631a54 | C | 4,141 | 134 | /*******************************************************************************
** Entropy.c
** Part of the mutual information toolbox
**
** Contains functions to calculate the entropy of a single variable H(X),
** the joint entropy of two variables H(X,Y), and the conditional entropy
** H(X|Y)
**
** Author:... |
b2291c289d1e7d9ef21ff37983cef079093ec43966b3a06c25e0fccc7cf13781 | C | 4,173 | 143 | #include <stdlib.h>
#include <stdio.h>
#include <time.h>
#include <mex.h>
#include <math.h>
#include "matrix.h"
#include "flsa.h"
/*
Functions contained in "flsa.h"
1. The algorithm for sloving (1) with a given (labmda1, lambda2)
void flsa(double *x, double *z, double *info,
double * v, double *z0,
... |
b4b316aca7892d5ccdc2d864d9e255f7362c5406a21595cb105a3922b663670a | C | 4,173 | 96 | /*******************************************************************************
** WeightedMutualInformation.c
** Part of the mutual information toolbox
**
** Contains functions to calculate the mutual information of
** two variables X and Y, I(X;Y), to calculate the joint mutual information
** of two variables X & Z... |
a1a378823057b3632f6e55def0c8d765c64e4bc1b7eb3be0fa9d61448eabe373 | C | 4,208 | 147 | /* daub4.c - Daubechies 4-coeff wavelet transform
*
* Applies the daubechies 4-coefficient multiscale wavelet transform on
* columns of the input matrix.
*
* USAGE
*
* [W] = daub4(D, [isign])
*
* Compile with:
*
* mex daub4.c
*
* INPUT
* D - signal matrix (each column of D is transformed ind... |
647a67e2bfed7ca4f02bc795ec83015b8e09fd378ee6b848cde4c7a4b3707a36 | C | 4,254 | 124 | #ifndef lint
static char svnid[] = "$Id: nifti_stats_mex.c 7147 2017-08-03 14:07:01Z spm $";
#endif
/*
* This is a MATLAB MEX interface for Bob Cox's extensive nifti_stats.c
* functionality. See nifti_stats.m for documentation.
*/
#include <stdio.h>
#include <stdlib.h>
#include <math.h>
#include "mex.h"
#include... |
7411a5fa52692baae622975b5ae666b3d5f88cc41355adaabefdb1aab7a5d2af | C | 4,294 | 138 | /* Takes a transform and a volume as the input, and outputs a tag file
which can be used by tagtoxfm. The primary goal is to compute a
linear transformation from a non-linear grid transform.
*/
#include <stdio.h>
#include <stdlib.h>
#include <unistd.h>
#define HAVE_MINC2 1
#include <volume_io.h>
#include <bicp... |
8ea96f854ff9039b286f16a376eb4edbb6024ad6af2c89edef9cafa96d42100d | C | 4,463 | 155 | #define PY_SSIZE_T_CLEAN
#include <Python.h>
#include <math.h>
#define NO_IMPORT_ARRAY
#define NO_IMPORT_UFUNC
#define PY_ARRAY_UNIQUE_SYMBOL shapely_ARRAY_API
#define PY_UFUNC_UNIQUE_SYMBOL shapely_UFUNC_API
#include <numpy/ndarraytypes.h>
#include <numpy/npy_3kcompat.h>
#include <numpy/ufuncobject.h>
#include "fas... |
64dbebe015919de040eb5fe3bb994134b491564447e8c423a3d09d0f8b91339d | C | 4,566 | 175 | <<<<<<< HEAD
/*
* $Id: zstream.c 7523 2019-02-01 11:31:08Z guillaume $
* Guillaume Flandin
*/
/* mex -O CFLAGS='$CFLAGS -std=c99' -largeArrayDims zstream.c */
/* setenv CFLAGS "`mkoctfile -p CFLAGS` -std=c99" */
/* mkoctfile --mex zstream.c */
/* miniz: https://github.com/richgel999/miniz */
#define MI... |
3de44c37c3cea8957964c2f266a69692e9df320623d941009032b91c5e466f55 | C | 4,701 | 151 | #define PY_SSIZE_T_CLEAN
#include <Python.h>
#include <math.h>
#define NO_IMPORT_ARRAY
#define NO_IMPORT_UFUNC
#define PY_ARRAY_UNIQUE_SYMBOL shapely_ARRAY_API
#define PY_UFUNC_UNIQUE_SYMBOL shapely_UFUNC_API
#include <numpy/ndarraytypes.h>
#include <numpy/npy_3kcompat.h>
#include <numpy/ufuncobject.h>
#include "fas... |
d3311fa2aff66e91ad51763f348d3a0484b60fe3e77f9233e32cbe60a7c59343 | C | 4,711 | 133 | /*******************************************************************************
** WeightedEntropy.c
** Part of the mutual information toolbox
**
** Contains functions to calculate the entropy of a single variable H(X),
** the joint entropy of two variables H(X,Y), and the conditional entropy
** H(X|Y), while using a... |
3f1c3b03c0c74109d9405910a9a3bc418489d9536fcd99b3346a8b745e47930a | C | 4,843 | 144 | /*******************************************************************************
** WeightedEntropy.c
** Part of the mutual information toolbox
**
** Contains functions to calculate the entropy of a single variable H(X),
** the joint entropy of two variables H(X,Y), and the conditional entropy
** H(X|Y), while using a... |
b7081b2782ce43f41ae796bb2dd200a48830f95ba24eff9043bc3221b7a5bde4 | C | 4,973 | 127 | /*******************************************************************************
** MIM implements the Mutual Information Maximisation method, which selects
** features with the largest univariate mutual informations.
**
** Initial Version - 22/02/2014
** Updated - 22/02/2014 - Patched calloc.
**
** Author - Adam Pococ... |
26f461b7bb370e50bfbbde406556ff47d3a1d449fadf239b4981b95dbee63db8 | C | 5,081 | 158 | /*******************************************************************************
** Demonstration feature selection algorithm - MATLAB r2009a
**
** Initial Version - 13/06/2008
** Updated - 07/07/2010
** based on CMIM.m
**
** Conditional Mutual Information Maximisation
** in
** "Fast Binary Feature Selection using Cond... |
277d405518640acbc80436721513ecaf9de21d984840c13d43cdabc101df14b0 | C | 5,095 | 141 | /****
* Needs two inputs: a structure segmentation map and
* a map with counts. Will then determine the total number
* of counts in a structure by adding up all counts for
* each of the labels/segmentations.
****/
#include <minc2.h>
#include <stdio.h>
#include <stdlib.h>
#include <math.h>
int main(int argc, char... |
ad4ca65cc1da8fdd8606006f4a38cbe7175e8a214d59da02f49cab0c51bc4850 | C | 5,113 | 121 | /*******************************************************************************
** MutualInformation.c
** Part of the mutual information toolbox
**
** Contains functions to calculate the mutual information of
** two variables X and Y, I(X;Y), to calculate the joint mutual information
** of two variables X & Z on the ... |
0cd259ee2d1d52036075d8b605542b393498aa1dd9cc750b5dd2c0d4fd28d20f | C | 5,130 | 159 | /*******************************************************************************
** Demonstration feature selection algorithm - MATLAB r2009a
**
** Initial Version - 13/06/2008
** Updated - 07/07/2010
** based on CMIM.m
**
** Conditional Mutual Information Maximisation
** in
** "Fast Binary Feature Selection using Cond... |
642e5da433d0ecb977beea6684c3aa74a7f74f91d8b5fae61123117acbd46c59 | C | 5,235 | 116 | /*******************************************************************************
** WeightedMutualInformation.c
** Part of the mutual information toolbox
**
** Contains functions to calculate the mutual information of
** two variables X and Y, I(X;Y), to calculate the joint mutual information
** of two variables X & Z... |
a04b80fe9f7e26263fdb5177081164d007bc75583774a8fe7a8916f0af6c4813 | C | 5,344 | 243 | #include <stdio.h>
#include <ctype.h>
#include <stdlib.h>
#include <string.h>
#include <errno.h>
#include "linear.h"
int print_null(const char *s,...) {return 0;}
static int (*info)(const char *fmt,...) = &printf;
struct feature_node *x;
int max_nr_attr = 64;
struct model* model_;
int flag_predict_probability=0;
v... |
969c4a504ba049b8185590b32338c5af1bcaecca4d4615b4c1b484f402892abe | C | 5,358 | 190 | /*******************************************************************************
** RenyiMIToolboxMex.c
** is the MATLAB entry point for the Renyi Entropy and MI MIToolbox functions
** when called from a MATLAB/OCTAVE script.
**
** Copyright 2010-2017 Adam Pocock, The University Of Manchester
** www.cs.manchester.ac.u... |
da234149ed8edaf19a44afb94539db02ec50f54b7ee0db53f518a73ca37bea2a | C | 5,381 | 229 | #include <stdio.h>
#include <ctype.h>
#include <stdlib.h>
#include <string.h>
#include <errno.h>
#include "svm.h"
struct svm_node *x;
int max_nr_attr = 64;
struct svm_model* model;
int predict_probability=0;
static char *line = NULL;
static int max_line_len;
static char* readline(FILE *input)
{
int len;
if(fget... |
3cd297eeba5e6e05456009108a4470ae9be5d5b9614a45bb95876041c7e37257 | C | 5,435 | 209 | #define PY_SSIZE_T_CLEAN
#include <Python.h>
#define PyGEOS_API_Module
#define PY_ARRAY_UNIQUE_SYMBOL shapely_ARRAY_API
#define PY_UFUNC_UNIQUE_SYMBOL shapely_UFUNC_API
#include <numpy/ndarraytypes.h>
#include <numpy/npy_3kcompat.h>
#include <numpy/ufuncobject.h>
#include "c_api.h"
#include "coords.h"
#include "geo... |
a1d0fa96a4422e4f350293f3206b029e09a54807067e7bba57d848e649f3e8dc | C | 5,450 | 139 | /*******************************************************************************
** CMIM.c, implements a discrete version of the
** Conditional Mutual Information Maximisation criterion, using the fast
** exact implementation from
**
** "Fast Binary Feature Selection using Conditional Mutual Information Maximisation"
... |
cd4ee9f656b50ebf5a7035f6a2f874454873fb8767d73bf7e3514e68d11724f2 | C | 5,653 | 184 | /*******************************************************************************
** Demonstration feature selection algorithm - MATLAB r2009a
**
** Initial Version - 13/06/2008
** Updated - 07/07/2010
** based on mRMR_D.m
**
** Minimum Relevance Maximum Redundancy
** in
** "Feature Selection Based on Mutual Information... |
3e816ffb2de443e97884a36567d2ea4cca2fac1223e065fd7bd4564201ab0c47 | C | 5,704 | 185 | /*******************************************************************************
** Demonstration feature selection algorithm - MATLAB r2009a
**
** Initial Version - 13/06/2008
** Updated - 07/07/2010
** based on mRMR_D.m
**
** Minimum Relevance Maximum Redundancy
** in
** "Feature Selection Based on Mutual Information... |
d2c425b2bc0bd08343a336a19e2a38a647b18aee58a145ad885f82b6d71736d5 | C | 5,811 | 178 | #include <stdio.h>
#include <stdlib.h>
#include <unistd.h>
#define HAVE_MINC2 1
#include <volume_io.h>
#include <bicpl.h>
#include <ParseArgv.h>
#include <time_stamp.h>
/* argument parsing defaults */
static int verbose = FALSE;
static int clobber = FALSE;
static int datatype = NC_SHORT;
/* argument table */
static... |
651adefce127a358a4b1e9282bac1a9d2a534d9cc5332b37040fbdc4bc1a8ed9 | C | 5,909 | 167 | /*******************************************************************************
** CondMI.c, implements the CMI criterion using a greedy forward search
**
** Initial Version - 19/08/2010
** Updated - 23/06/2011
** Updated - 22/02/2014 - Patched calloc.
**
** Author - Adam Pocock
**
** Part of the FEAture Selection To... |
8554baa537524ecaba1e0eb5e860fcdb06e0ca6edc89f11482bce869f78bedcf | C | 5,943 | 205 | /*******************************************************************************
**
** RenyiMIToolboxMex.c
** is the MATLAB entry point for the Renyi Entropy and MI MIToolbox functions
** when called from a MATLAB/OCTAVE script.
**
** Copyright 2010 Adam Pocock, The University Of Manchester
** www.cs.manchester.a... |
1918a4abf70dc8779f30b781f633dabaacec9a6a42d9b056f8d546b16bf75ec3 | C | 5,963 | 192 | /*******************************************************************************
** RenyiEntropy.c
** Part of the mutual information toolbox
**
** Contains functions to calculate the Renyi alpha entropy of a single variable
** H_\alpha(X), the Renyi joint entropy of two variables H_\alpha(X,Y), and the
** conditional... |
0c3814519b3b9cbd6c6edaee0fb14a5842ee47210643fe036241ac75f52736ca | C | 6,136 | 168 | /*******************************************************************************
** mRMR_D.c implements the minimum Relevance Maximum Redundancy criterion
** using the difference variant, from
**
** "Feature Selection Based on Mutual Information: Criteria of Max-Dependency, Max-Relevance, and Min-Redundancy"
** H. Peng... |
199eca215f904b1336f815120fecdff50c785fca72634cf00fc5318e8eb717ad | C | 6,174 | 173 | /*******************************************************************************
** JMI.c implements the JMI criterion from
**
** "Data Visualization and Feature Selection: New Algorithms for Nongaussian Data"
** H. Yang and J. Moody, NIPS (1999)
**
** Initial Version - 19/08/2010
** Updated - 23/06/2011
** Updated - 2... |
c6381e34c625d4694af5683333b67f58058807fc7507d2bfe4b4cdfcbebb64b3 | C | 6,223 | 199 | /*******************************************************************************
** Demonstration feature selection algorithm - MATLAB r2009a
**
** Initial Version - 13/06/2008
** Updated - 07/07/2010
** based on DISR.m
**
** Double Input Symmetrical Relevance
** in
** "On the Use of Variable Complementarity for Featur... |
c07e3d7d05eca2e565886fe662d4929a9b35d15a4cdd65bdcc5946b073f9bd7a | C | 6,235 | 189 | /* ----------------------------- MNI Header -----------------------------------
@NAME : tagtoxfm
@INPUT : argc, argv - command line arguments
@OUTPUT : (none)
@RETURNS : status
@DESCRIPTION: Program to calculate a transform file from a tag file.
@METHOD :
@GLOBALS :
@CALLS :
@CREATED ... |
1be243ada5c695b2ef1ecd98c21cdc6ed583a92c7c1e2d7609a54e498e1fb67e | C | 6,264 | 252 | #define PY_SSIZE_T_CLEAN
#include <Python.h>
#define NO_IMPORT_ARRAY
#define PY_ARRAY_UNIQUE_SYMBOL shapely_ARRAY_API
#include <numpy/ndarraytypes.h>
#include <numpy/npy_math.h>
#include "geos.h"
/* Check simple geometries (Point, LineString, LinearRing; backed by a single CoordSequence).
Returns 1 on true, 0 on ... |
b058b6ce1ebca3af055acb281ec39456dd57431fe459c801aa7ff535ab9d2214 | C | 6,292 | 231 | /*
*
* mexCCACollectdata.c
* prepare data for cca.m to form SDP
*
* by feisha@cis.upenn.edu
*/
#include "mex.h"
#include "matrix.h"
#include <stdlib.h>
#include <float.h>
#include <string.h>
#include <math.h>
/* the computation engine */
void collectdata(double *y, int* edgerow, int *edgecol, double *... |
eb234c353d4593c6bb7768748b650f758184b75b8dad1a1a99cbed614f217ad1 | C | 6,399 | 202 | /*******************************************************************************
** Demonstration feature selection algorithm - MATLAB r2009a
**
** Initial Version - 13/06/2008
** Updated - 07/07/2010
** based on DISR.m
**
** Double Input Symmetrical Relevance
** in
** "On the Use of Variable Complementarity for Featur... |
f8950f787d58fd66a7750d2ee6c98688fb8594cec84006ab00da330c40687b9b | C | 6,460 | 201 | #define PY_SSIZE_T_CLEAN
#include <Python.h>
#include <math.h>
#define NO_IMPORT_ARRAY
#define NO_IMPORT_UFUNC
#define PY_ARRAY_UNIQUE_SYMBOL shapely_ARRAY_API
#define PY_UFUNC_UNIQUE_SYMBOL shapely_UFUNC_API
#include <numpy/ndarraytypes.h>
#include <numpy/npy_3kcompat.h>
#include <numpy/ufuncobject.h>
#include "fas... |
d7b65b54bb3ca0d109388aebd0bef6de4542184284db1e14132127b07440dec0 | C | 6,493 | 180 | /*******************************************************************************
** DISR.c, implements the Double Input Symmetrical Relevance criterion
** from
**
** "On the Use of Variable Complementarity for Feature Selection in Cancer Classification"
** P. Meyer and G. Bontempi, (2006)
**
** Initial Version - 13/06... |
c0308d4d77fc282fbf3b0a86e09703b0320e1cd7e6ec93799ea1cfee76b1ffbb | C | 6,700 | 341 | #include <float.h>
#include <stdio.h>
#include <stdlib.h>
#include <ctype.h>
#include <string.h>
void exit_with_help()
{
printf(
"Usage: svm-scale [options] data_filename\n"
"options:\n"
"-l lower : x scaling lower limit (default -1)\n"
"-u upper : x scaling upper limit (default +1)\n"
"-y y_lower y_upper : y sc... |
d81e984da666b2e4f1905d171e4bafb8b04103969ad1f7279801a92061c81eba | C | 6,727 | 252 | /* L.c
List data structure and utility functions.
Elements can only be added or deleted from end
To create a new linked list:
L = NEW_L(N);
where N is the number of nodes initially allocated
Functions:
NEW_L - make a new linked list
L_GET_ID - get the id of the i-th node
... |
361db39fd71b318f216dfa50812698d5dd6af28941f10345144298a744622ad4 | C | 7,042 | 353 | #include <float.h>
#include <stdio.h>
#include <stdlib.h>
#include <ctype.h>
#include <string.h>
void exit_with_help()
{
printf(
"Usage: svm-scale [options] data_filename\n"
"options:\n"
"-l lower : x scaling lower limit (default -1)\n"
"-u upper : x scaling upper limit (default +1)\n"
"-y y_lower y_upper : y sc... |
a1da21527674ef9a684e48bb8395e4952cd6f8550de4fc61c6a7fa38dbe6ec43 | C | 7,042 | 353 | #include <float.h>
#include <stdio.h>
#include <stdlib.h>
#include <ctype.h>
#include <string.h>
void exit_with_help()
{
printf(
"Usage: svm-scale [options] data_filename\n"
"options:\n"
"-l lower : x scaling lower limit (default -1)\n"
"-u upper : x scaling upper limit (default +1)\n"
"-y y_lower y_upper : y sc... |
299e3d8c9512160afc8020b9171322a657ddca78aa9cb5c895d3869d005a4932 | C | 7,045 | 269 | /***************************************************************************
*cr
*cr (C) Copyright 1995-2009 The Board of Trustees of the
*cr University of Illinois
*cr All Rights Reserved
*cr
***************************************************************... |
f4aed0451e08aa50888f593232fe6ff0c7fb217c8b64f4cd750194759b6dadad | C | 7,053 | 253 | #include "mex.h"
#include "math.h"
#include "string.h"
void computeKernelRow(double* X, int n, int d, int index, const char* function, int param1, int param2, double* row);
void computeColumnSums(double* X, int n, int d, const char* function, int param1, int param2, double* column_sums, double *total_sum);
void cente... |
3258c6c33f9b1f73062106e573ff5a3e574bec6b24ddfb69d5157d33fe6414c9 | C | 7,359 | 181 | /*******************************************************************************
** ICAP.c implements the Interaction Capping criterion from
**
** "Machine Learning Based on Attribute Interactions"
** A. Jakulin, PhD Thesis (2005)
**
** Initial Version - 19/08/2010
** Updated - 12/02/2013 - patched the use of DBL_MAX... |
ad257a64564a27a5fa665f0b5a772e8d3cb56b06e2ba367381ee863f788349cb | C | 7,684 | 349 | #include <stdlib.h>
#include <string.h>
#include "svm.h"
#include "mex.h"
#if MX_API_VER < 0x07030000
typedef int mwIndex;
#endif
#define NUM_OF_RETURN_FIELD 10
#define Malloc(type,n) (type *)malloc((n)*sizeof(type))
static const char *field_names[] = {
"Parameters",
"nr_class",
"totalSV",
"rho",
"Label",
"P... |
6799ba7a1e752afc51685758e30a994075c6bf1d153ca720652fcb4f2d9dc4b4 | C | 7,722 | 351 | #include <stdlib.h>
#include <string.h>
#include "../svm.h"
#include "mex.h"
#ifdef MX_API_VER
#if MX_API_VER < 0x07030000
typedef int mwIndex;
#endif
#endif
#define NUM_OF_RETURN_FIELD 10
#define Malloc(type,n) (type *)malloc((n)*sizeof(type))
static const char *field_names[] = {
"Parameters",
"nr_class",
"tot... |
d226c1f140d02b3bb5d7f6ec438e62aa5140097b9228da2be94441763553bf39 | C | 7,787 | 331 | #include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <ctype.h>
#include "svm.h"
#define Malloc(type,n) (type *)malloc((n)*sizeof(type))
void exit_with_help()
{
printf(
"Usage: svm-train [options] training_set_file [model_file]\n"
"options:\n"
"-s svm_type : set type of SVM (default 0)\n"
"\t%d -- C... |
db508a5a25d294b4a96314460c4797b3b992554772f7601687c6967e3c9cbb07 | C | 7,844 | 196 | /*******************************************************************************
** betaGamma() implements the Beta-Gamma space from Brown (2009).
** This incoporates MIFS, CIFE, and CondRed.
**
** MIFS - "Using mutual information for selecting features in supervised neural net learning"
** R. Battiti, IEEE Transaction... |
a7a338b213957835168e9a4ca7ef20ca84b3dad50300f08b671fef166a7c1eb2 | C | 8,098 | 246 | #define PY_SSIZE_T_CLEAN
#include <Python.h>
#include <math.h>
#define NO_IMPORT_ARRAY
#define NO_IMPORT_UFUNC
#define PY_ARRAY_UNIQUE_SYMBOL shapely_ARRAY_API
#define PY_UFUNC_UNIQUE_SYMBOL shapely_UFUNC_API
#include <numpy/ndarraytypes.h>
#include <numpy/npy_3kcompat.h>
#include <numpy/ufuncobject.h>
#include "fas... |
6aa2e9cbcf3d656e3801e8d75165e5b6e74040f2380ea90c88fb3bdbbff9e806 | C | 8,141 | 365 | #include <stdlib.h>
#include <string.h>
#include "svm.h"
#include "mex.h"
#if MX_API_VER < 0x07030000
typedef int mwIndex;
#endif
#define NUM_OF_RETURN_FIELD 10
#define Malloc(type,n) (type *)malloc((n)*sizeof(type))
static const char *field_names[] = {
"Parameters",
"nr_class",
"totalSV",
"rho",
"Label",
"... |
a8bd0b875a6477f1f82fc56e6bc820fe879a8b0847ac6b16a446685358930b8a | C | 8,151 | 281 | /*
*
* mexCCACollectdata.c
* prepare data for cca.m to form SDP
*
* by feisha@cis.upenn.edu
*/
#include "mex.h"
#include "matrix.h"
#include <stdlib.h>
#include <float.h>
#include <string.h>
#include <math.h>
/* the computation engine */
void collectdata(double *x, double *y, int* edgerow, int *edgeco... |
00b49f2ef5faa6ba0dc63da7cf5174b41ecd0f9015a7a724ef5dc8558e9be7d1 | C | 8,186 | 339 | /*
* Miniscope_PYTHON_I2C_2_SPI.c
*
* Created: 7/21/2018 6:34:01 PM
* Author : DBAharoni
*/
// ----- USER DEFINED CONFIG (SELECT ONE) ------
//#define DUAL_LED_MINISCOPE
//#define DUAL_LED_MINISCOPE_DEMO
//#define V4_MINISCOPE
#define V4_MINISCOPE_CONST_LED
// ---------------------------------------------
#defin... |
4b856c68c031a26df88fb62cbe8b95d366b5324fad2e51c7b927a701e353fb5e | C | 8,491 | 339 | #include <stdio.h>
#include <ctype.h>
#include <stdlib.h>
#include <string.h>
#include "svm.h"
#include "mex.h"
#include "svm_model_matlab.h"
#if MX_API_VER < 0x07030000
typedef int mwIndex;
#endif
#define CMD_LEN 2048
void read_sparse_instance(const mxArray *prhs, int index, struct svm_node *x)
{
int i, j, low, h... |
b14555e84b4c62d6a30a8c7dc0dddd07e18f0f3dd55dd711b2d662ca3db9c6b5 | C | 8,523 | 341 | #include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include "linear.h"
#include "mex.h"
#include "linear_model_matlab.h"
#ifdef MX_API_VER
#if MX_API_VER < 0x07030000
typedef int mwIndex;
#endif
#endif
#define CMD_LEN 2048
#define Malloc(type,n) (type *)malloc((n)*sizeof(type))
int print_null(const char *s... |
3644c84a34aff86e68c30bbb5e36986b9d503e9898642f18f9251db83c001189 | C | 8,557 | 317 | #include <stdlib.h>
#include <stdio.h>
#include <time.h>
#include <mex.h>
#include <math.h>
#include "matrix.h"
#define delta 1e-12
/*
Euclidean Projection onto l1 Ball (eplb)
min 1/2 ||x- y||_2^2
s.t. ||x||_1 <= z
which is converted to the following zero finding problem
f(lambda)= sum... |
60fb80c83834fb5ba9dba872062d31063654a88aa3afdf0081890c94df9c4c65 | C | 8,696 | 405 | #include <float.h>
#include <stdio.h>
#include <stdlib.h>
#include <ctype.h>
#include <string.h>
void exit_with_help()
{
printf(
"Usage: svm-scale [options] data_filename\n"
"options:\n"
"-l lower : x scaling lower limit (default -1)\n"
"-u upper : x scaling upper limit (default +1)\n"
"-y y_lower y_upper : y sc... |
a7a9f12471dc622f2c15eedf286c709b4763412d6f0891f0967531747c6f57f0 | C | 9,058 | 345 | #include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include "svm.h"
#include "mex.h"
#include "svm_model_matlab.h"
#if MX_API_VER < 0x07030000
typedef int mwIndex;
#endif
#define CMD_LEN 2048
void read_sparse_instance(const mxArray *prhs, int index, struct svm_node *x)
{
int i, j, low, high;
mwIndex *ir, ... |
220c309fa3cd2d2ad8a3e1873421682841f6e74a76315b3d841daa6e7876b4a2 | C | 9,072 | 345 | #include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include "svm.h"
#include "mex.h"
#include "svm_model_matlab.h"
#if MX_API_VER < 0x07030000
typedef int mwIndex;
#endif
#define CMD_LEN 2048
void read_sparse_instance(const mxArray *prhs, int index, struct svm_node *x)
{
int i, j, low, high;
mwIndex *ir, ... |
8702a4f12cfdae3b6fe7516bd5999395e4aa054803512033297cbd7725bbb79e | C | 9,093 | 279 | #define PY_SSIZE_T_CLEAN
#include <Python.h>
#include <math.h>
#define NO_IMPORT_ARRAY
#define NO_IMPORT_UFUNC
#define PY_ARRAY_UNIQUE_SYMBOL shapely_ARRAY_API
#define PY_UFUNC_UNIQUE_SYMBOL shapely_UFUNC_API
#include <numpy/ndarraytypes.h>
#include <numpy/npy_3kcompat.h>
#include <numpy/ufuncobject.h>
#include "fas... |
11fcab9f52de774c86b09df8d4b0eb552c14c553b67d9b019c7ad4c7aad2f9f8 | C | 9,174 | 394 | #include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <ctype.h>
#include <errno.h>
#include "svm.h"
#define Malloc(type,n) (type *)malloc((n)*sizeof(type))
void print_null(const char *s) {}
void exit_with_help()
{
printf(
"Usage: svm-train [options] training_set_file [model_file]\n"
"options:\n"
"-s... |
8816831ca4768131e25984c7b20626dc31e8275f253f54beaba1df441053ae7e | C | 9,284 | 284 | #define PY_SSIZE_T_CLEAN
#include <Python.h>
#include <math.h>
#define NO_IMPORT_ARRAY
#define NO_IMPORT_UFUNC
#define PY_ARRAY_UNIQUE_SYMBOL shapely_ARRAY_API
#define PY_UFUNC_UNIQUE_SYMBOL shapely_UFUNC_API
#include <numpy/ndarraytypes.h>
#include <numpy/npy_3kcompat.h>
#include <numpy/ufuncobject.h>
#include "fas... |
4c39392041fd52ee15ab6f5702b480cc1c5601d3db67d0efa6b821c36c6bc310 | C | 9,297 | 349 | #include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include "../svm.h"
#include "mex.h"
#include "svm_model_matlab.h"
#ifdef MX_API_VER
#if MX_API_VER < 0x07030000
typedef int mwIndex;
#endif
#endif
#define CMD_LEN 2048
void read_sparse_instance(const mxArray *prhs, int index, struct svm_node *x)
{
int i, ... |
caaab70b1ac88088ae5972428e0e76393bc7acbc9cf4e2a982e04d0cc21d4023 | C | 9,315 | 423 | #include <stdlib.h>
#include <string.h>
#include "svm.h"
#include "mex.h"
#if MX_API_VER < 0x07030000
typedef int mwIndex;
#endif
#define NUM_OF_RETURN_FIELD 11
#define Malloc(type,n) (type *)malloc((n)*sizeof(type))
static const char *field_names[] = {
"Parameters",
"nr_class",
"totalSV",
"rho",
"w2",
"Lab... |
9d5b92c5c5b2d44f47848fff1e3d4d33ded77c5cee76b3ef308ee9d7cf9b3339 | C | 9,322 | 269 | #define PY_SSIZE_T_CLEAN
#include <Python.h>
#include <math.h>
#define NO_IMPORT_ARRAY
#define NO_IMPORT_UFUNC
#define PY_ARRAY_UNIQUE_SYMBOL shapely_ARRAY_API
#define PY_UFUNC_UNIQUE_SYMBOL shapely_UFUNC_API
#include <numpy/ndarraytypes.h>
#include <numpy/npy_3kcompat.h>
#include <numpy/ufuncobject.h>
#include "fas... |
c114ef436c993423f08f51e3c63ca86a7867e3404f9233e526e2ea0ff6e39032 | C | 9,422 | 311 | /*******************************************************************************
** ArrayOperations.c
** Part of the mutual information toolbox
**
** Contains functions to floor arrays, and to merge arrays into a joint
** state.
**
** Author: Adam Pocock
** Created 17/2/2010
** Updated - 22/02/2014 - Added checking on... |
8196ea7cb359d53e3373e6fd0d4cfe858ff6397e40f18d59477295a0785395dd | C | 9,477 | 269 | #define PY_SSIZE_T_CLEAN
#include <Python.h>
#include <math.h>
#define NO_IMPORT_ARRAY
#define NO_IMPORT_UFUNC
#define PY_ARRAY_UNIQUE_SYMBOL shapely_ARRAY_API
#define PY_UFUNC_UNIQUE_SYMBOL shapely_UFUNC_API
#include <numpy/ndarraytypes.h>
#include <numpy/npy_3kcompat.h>
#include <numpy/ufuncobject.h>
#include "fas... |
3790767862b809c960fe2bf3b9200aafe240e812bdd254a90c4bb38b40756d40 | C | 9,533 | 385 | /*
* $Id: mat2file.c 7510 2019-01-02 15:06:12Z guillaume $
* John Ashburner
*/
#define _LARGEFILE_SOURCE
#define _LARGEFILE64_SOURCE
#define _FILE_OFFSET_BITS 64
#include <math.h>
#include <fcntl.h>
#include <sys/stat.h>
#include <stdlib.h>
#include <stdio.h>
#include "mex.h"
#ifdef SPM_WIN32
#include <windows.h>
... |
15a7535d83af4aa78d7223f341943578d3bf6fbabc0630d565af5ca61dd65973 | C | 9,784 | 291 | #define PY_SSIZE_T_CLEAN
#include <Python.h>
#include <math.h>
#define NO_IMPORT_ARRAY
#define NO_IMPORT_UFUNC
#define PY_ARRAY_UNIQUE_SYMBOL shapely_ARRAY_API
#define PY_UFUNC_UNIQUE_SYMBOL shapely_UFUNC_API
#include <numpy/ndarraytypes.h>
#include <numpy/npy_3kcompat.h>
#include <numpy/ufuncobject.h>
#include "fas... |
59376a0108c27e512de5f1a24581ed2aeb66748579328f09d994457605ee444c | C | 9,948 | 265 | #define PY_SSIZE_T_CLEAN
#include <Python.h>
#include <math.h>
#define NO_IMPORT_ARRAY
#define NO_IMPORT_UFUNC
#define PY_ARRAY_UNIQUE_SYMBOL shapely_ARRAY_API
#define PY_UFUNC_UNIQUE_SYMBOL shapely_UFUNC_API
#include <numpy/ndarraytypes.h>
#include <numpy/npy_3kcompat.h>
#include <numpy/ufuncobject.h>
#include "fas... |
1dfb561656ad0fa3e35f00d613f6b60c6a2979bac518b487f34bc07f99570d67 | C | 10,284 | 285 | #define PY_SSIZE_T_CLEAN
#include <Python.h>
#include <math.h>
#define NO_IMPORT_ARRAY
#define NO_IMPORT_UFUNC
#define PY_ARRAY_UNIQUE_SYMBOL shapely_ARRAY_API
#define PY_UFUNC_UNIQUE_SYMBOL shapely_UFUNC_API
#include <numpy/ndarraytypes.h>
#include <numpy/npy_3kcompat.h>
#include <numpy/ufuncobject.h>
#include "fas... |
ac33e9915ed7f4bee36b57575aa90b8d22d8c9cd09fdaff7d66230a2e9b2d198 | C | 10,296 | 292 | #define PY_SSIZE_T_CLEAN
#include <Python.h>
#include <math.h>
#define NO_IMPORT_ARRAY
#define NO_IMPORT_UFUNC
#define PY_ARRAY_UNIQUE_SYMBOL shapely_ARRAY_API
#define PY_UFUNC_UNIQUE_SYMBOL shapely_UFUNC_API
#include <numpy/ndarraytypes.h>
#include <numpy/npy_3kcompat.h>
#include <numpy/ufuncobject.h>
#include "fas... |
7a696fc25c8e097a071f38da387a3bad79b24078395c90e2605d2c2dfe1eefce | C | 10,384 | 315 | #define PY_SSIZE_T_CLEAN
#include <Python.h>
#include <math.h>
#define NO_IMPORT_ARRAY
#define NO_IMPORT_UFUNC
#define PY_ARRAY_UNIQUE_SYMBOL shapely_ARRAY_API
#define PY_UFUNC_UNIQUE_SYMBOL shapely_UFUNC_API
#include <numpy/ndarraytypes.h>
#include <numpy/npy_3kcompat.h>
#include <numpy/ufuncobject.h>
#include "fas... |
12a0118179ca1052921f6020fd0c0c96555acb1912d18441f7a067e7e4c9a79e | C | 11,118 | 347 | /*******************************************************************************
** CalculateProbability.c
** Part of the mutual information toolbox
**
** Contains functions to calculate the probability of each state in the array
** and to calculate the probability of the joint state of two arrays
**
** Author: Adam P... |
703e2aef37354ef06482df919835f9e660b02d7d342f50fddfde0db6a505299c | C | 11,194 | 340 | #define PY_SSIZE_T_CLEAN
#include <Python.h>
#include <math.h>
#define NO_IMPORT_ARRAY
#define NO_IMPORT_UFUNC
#define PY_ARRAY_UNIQUE_SYMBOL shapely_ARRAY_API
#define PY_UFUNC_UNIQUE_SYMBOL shapely_UFUNC_API
#include <numpy/ndarraytypes.h>
#include <numpy/npy_3kcompat.h>
#include <numpy/ufuncobject.h>
#include "fas... |
40c7fd09342e7b6e6f4ad67ba0c4e0edacde653ac33a29f2a8a15119673d7122 | C | 11,255 | 467 | #include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <ctype.h>
#include "svm.h"
#include "mex.h"
#include "svm_model_matlab.h"
#if MX_API_VER < 0x07030000
typedef int mwIndex;
#endif
#define CMD_LEN 2048
#define Malloc(type,n) (type *)malloc((n)*sizeof(type))
void exit_with_help()
{
mexPrintf(
"Usa... |
ed13bec9baf9b0dad61197fe7cec6f1d60f33ea25c9b33be266acb49f72b73fc | C | 11,338 | 304 | #define PY_SSIZE_T_CLEAN
#include <Python.h>
#include <math.h>
#define NO_IMPORT_ARRAY
#define NO_IMPORT_UFUNC
#define PY_ARRAY_UNIQUE_SYMBOL shapely_ARRAY_API
#define PY_UFUNC_UNIQUE_SYMBOL shapely_UFUNC_API
#include <numpy/ndarraytypes.h>
#include <numpy/npy_3kcompat.h>
#include <numpy/ufuncobject.h>
#include "fas... |
d886d307e39c5aa5eb3fa1bf54aef89b3438c6236924d8eb2f8f53f051e5ff15 | C | 11,570 | 478 | #include <stdio.h>
#include <math.h>
#include <stdlib.h>
#include <string.h>
#include <ctype.h>
#include <errno.h>
#include "linear.h"
#define Malloc(type,n) (type *)malloc((n)*sizeof(type))
#define INF HUGE_VAL
void print_null(const char *s) {}
void exit_with_help()
{
printf(
"Usage: train [options] training_set_f... |
162d9735fe3e443b1ea2ba8116f74d9938120e06903856f24600ea24216d31a2 | C | 11,913 | 334 | #define PY_SSIZE_T_CLEAN
#include <Python.h>
#include <math.h>
#define NO_IMPORT_ARRAY
#define NO_IMPORT_UFUNC
#define PY_ARRAY_UNIQUE_SYMBOL shapely_ARRAY_API
#define PY_UFUNC_UNIQUE_SYMBOL shapely_UFUNC_API
#include <numpy/ndarraytypes.h>
#include <numpy/npy_3kcompat.h>
#include <numpy/ufuncobject.h>
#include "fas... |
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