#include <math.h>
#include "mex.h"
double mymax(double x, double y)
{
if (x > y)
return x;
else
return y;
}
double absolute(double x)
{
if (x >= -x)
return x;
else
return -x;
}
void permuteInt(int *x, int p, int q)
{
int temp;
temp = x[p];
x[p] = x[q];
x[q] = temp;
}
void permute(double *x, int p, int q)
{
double temp;
temp = x[p];
x[p] = x[q];
x[q] = temp;
}
void permuteRows(double *x, int p, int q,int n)
{
int i;
double temp;
for(i = 0; i < n; i++)
{
temp = x[p+i*n];
x[p+i*n] = x[q+i*n];
x[q+i*n] = temp;
}
}
void permuteCols(double *x, int p, int q,int n)
{
int i;
double temp;
for(i = 0; i < n; i++)
{
temp = x[i+p*n];
x[i+p*n] = x[i+q*n];
x[i+q*n] = temp;
}
}
void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[])
{
int n,sizL[2],sizD[2],i,j,q,s,
*P;
double mu,gamma,xi,delta,beta,maxVal,theta,
*c, *H, *L, *D, *A;
/* Input */
H = mxGetPr(prhs[0]);
if (nrhs == 1)
{
mu = 1e-12;
}
else
{
mu = mxGetScalar(prhs[1]);
}
/* Compute Sizes */
n = mxGetDimensions(prhs[0])[0];
/* Form Output */
sizL[0] = n;
sizL[1] = n;
plhs[0] = mxCreateNumericArray(2,sizL,mxDOUBLE_CLASS,mxREAL);
L = mxGetPr(plhs[0]);
sizD[0] = n;
sizD[1] = 1;
plhs[1] = mxCreateNumericArray(2,sizD,mxDOUBLE_CLASS,mxREAL);
D = mxGetPr(plhs[1]);
plhs[2] = mxCreateNumericArray(2,sizD,mxINT32_CLASS,mxREAL);
P = (int*)mxGetData(plhs[2]);
/* Initialize */
c = mxCalloc(n*n,sizeof(double));
A = mxCalloc(n*n,sizeof(double));
for (i = 0; i < n; i++)
{
P[i] = i;
for (j = 0;j < n; j++)
{
A[i+n*j] = H[i+n*j];
}
}
gamma = 0;
for (i = 0; i < n; i++)
{
L[i+n*i] = 1;
c[i+n*i] = A[i+n*i];
}
/* Compute modification parameters */
gamma = -1;
xi = -1;
for (i = 0; i < n; i++)
{
gamma = mymax(gamma,absolute(A[i+n*i]));
for (j = 0;j < n; j++)
{
//printf("A(%d,%d) = %f, %f\n",i,j,A[i+n*j],absolute(A[i+n*j]));
if (i != j)
xi = mymax(xi,absolute(A[i+n*j]));
}
}
delta = mu*mymax(gamma+xi,1);
if (n > 1)
{
beta = sqrt(mymax(gamma,mymax(mu,xi/sqrt(n*n-1))));
}
else
{
beta = sqrt(mymax(gamma,mu));
}
for (j = 0; j < n; j++)
{
/* Find q that results in Best Permutation with j */
maxVal = -1;
q = 0;
for(i = j; i < n; i++)
{
if (absolute(c[i+n*i]) > maxVal)
{
maxVal = mymax(maxVal,absolute(c[i+n*i]));
q = i;
}
}
/* Permute D,c,L,A,P */
permute(D,j,q);
permuteInt(P,j,q);
permuteRows(c,j,q,n);
permuteCols(c,j,q,n);
permuteRows(L,j,q,n);
permuteCols(L,j,q,n);
permuteRows(A,j,q,n);
permuteCols(A,j,q,n);
for(s = 0; s <= j-1; s++)
L[j+n*s] = c[j+n*s]/D[s];
for(i = j+1; i < n; i++)
{
c[i+j*n] = A[i+j*n];
for(s = 0; s <= j-1; s++)
{
c[i+j*n] -= L[j+n*s]*c[i+n*s];
}
}
theta = 0;
if (j < n-1)
{
for(i = j+1;i < n; i++)
theta = mymax(theta,absolute(c[i+n*j]));
}
D[j] = mymax(absolute(c[j+n*j]),mymax(delta,theta*theta/(beta*beta)));
if (j < n-1)
{
for(i = j+1; i < n; i++)
{
c[i+n*i] = c[i+n*i] - c[i+n*j]*c[i+n*j]/D[j];
}
}
}
for(i = 0; i < n; i++)
P[i]++;
mxFree(c);
mxFree(A);
}
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mnist数据库的深度学习网络训练和识别matlab2021a仿真_源码.zip (61个子文件)
mnist数据库的深度学习网络训练和识别matlab2021a仿真_源码
sparseAutoencoderCost.m 4KB
softmaxProbability.m 212B
softmaxTrain.m 2KB
softmaxCost.m 1KB
loadMNISTLabels.m 516B
initializeParameters.m 622B
minFunc
rosenbrock.m 1KB
lbfgsC.c 2KB
lbfgs.m 885B
mcholC.mexw32 8KB
minFunc_processInputOptions.m 3KB
mcholC.mexmaci64 13KB
lbfgsC.mexw64 10KB
precondTriu.m 50B
lbfgsC.mexmaci 12KB
ArmijoBacktrack.m 3KB
mcholinc.m 539B
autoHv.m 307B
WolfeLineSearch.m 11KB
isLegal.m 106B
precondTriuDiag.m 59B
lbfgsUpdate.m 594B
lbfgsC.mexa64 8KB
lbfgsC.mexw32 7KB
taylorModel.m 677B
precondDiag.m 41B
autoGrad.m 807B
example_minFunc.m 2KB
logistic
repmatC.dll 8KB
repmatC.mexglx 20KB
LogisticHv.m 208B
repmatC.mexmac 10KB
repmatC.c 4KB
LogisticDiagPrecond.m 397B
mexutil.h 309B
LogisticLoss.m 625B
mylogsumexp.m 219B
mexutil.c 1KB
lbfgsC.mexmac 9KB
autoTensor.m 870B
conjGrad.m 2KB
callOutput.m 374B
lbfgsC.mexglx 8KB
dampedUpdate.m 953B
autoHess.m 901B
mcholC.mexw64 12KB
mcholC.c 4KB
minFunc.m 41KB
lbfgsC.mexmaci64 9KB
example_minFunc_LR.m 2KB
mchol.m 1KB
polyinterp.m 4KB
feedForwardAutoencoder.m 1KB
Runme.m 6KB
loadMNISTImages.m 811B
opttheta.mat 2.31MB
softmaxPredict.m 779B
display_network.m 3KB
autoencoder.png 64KB
mnist
train-images-idx3-ubyte 44.86MB
train-labels-idx1-ubyte 59KB
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