代码搜索：Models
找到约 5,847 项符合「Models」的源代码
代码结果 5,847
www.eeworm.com/read/375212/9369007
m contents.m
% PLS_Toolbox.
% Version 1.5.1 22-October-95
% Copyright (c) 1995 by Eigenvector Technologies
% Barry M. Wise and Neal B. Gallagher
%
% Data Scaling and Preprocessing.
% auto - Autoscales
www.eeworm.com/read/374010/9423711
m wrescale.m
function [W1,W2]=wrescale(W1,W2,Uscale,Yscale,NN)
% WRESCALE
% --------
% Rescale weights of a trained network so that the network
% can work on unscaled data.
%
% Calling [W1,W2
www.eeworm.com/read/374010/9423734
readme
--OO--
Magnus Norgaard's
NEURAL NETWORK BASED SYSTEM IDENTIFICATION TOOLBOX
www.eeworm.com/read/176784/9484218
java polygonmodeling.java
//made by Hu Pan 2004.9.8
import java.awt.*;
import java.awt.event.*;
import java.applet.*;
public class polygonModeling extends Applet implements KeyListener, ActionListener{
private Ima
www.eeworm.com/read/170249/9813363
readme
--OO--
Magnus Norgaard's
NEURAL NETWORK BASED SYSTEM IDENTIFICATION TOOLBOX
www.eeworm.com/read/365849/9843657
txt how-to-start.txt
First, make sure that the following directories are added to your path:
* expressions
* filters
* GUI
* models
* noise
* resampling
* simulators
There is a script for doing this named "pfp
www.eeworm.com/read/362500/9995916
m rinverse.m
function rinv = rinverse(p,t,w,f);
%RINVERSE Calculates pseudo inverse for PLS, PCR and RR models
% Inverse calculated depends upon the number of inputs supplied.
% For PLS models, the inputs are
www.eeworm.com/read/165806/10050953
java buttonfilter.java
/*
* @(#)ButtonFilter.java 1.14 04/07/26
*
* Copyright (c) 2004 Sun Microsystems, Inc. All Rights Reserved.
*
* Redistribution and use in source and binary forms, with or without
* modificatio
www.eeworm.com/read/164583/10100650
py mixture.py
#!/usr/bin/env python2.3
#
# ghmm example: mixture.py
#
#
#
#
from ghmm import *
import numarray
import math
import getopt, sys, string
def Entropy(prob_dist):
""" Returns Entropy for the discre
www.eeworm.com/read/161189/10439819
m examp.m
%
% Reset to a known state.
%
clear
rand('state',0);
randn('state',0);
%
% Make X, Y, and SIGMA global.
%
global X;
global Y;
global SIGMA;
%
% Generate the data set.
%
X=(1.0:0.25:7.0)';
ptrue=[1.0;