int main()
{
const unsigned int SAMPLE_NUM = 1000;
// 定义训练输入矩阵和输出矩阵
LPerceptronMatrix X(SAMPLE_NUM, 2);
LPerceptronMatrix Y(SAMPLE_NUM, 1);
for (unsigned int i = 0; i < SAMPLE_NUM; i++)
{
X[i][0] = (float)RandInt(-1000, 1000);
X[i][1] = (float)RandInt(-1000, 1000);
Y[i][0] = ((X[i][0] + X[i][1]) >= 0)? LPERCEPTRON_SUN : LPERCEPTRON_MOON;
}
LPerceptronProblem problem(X, Y);
// 训练模型
LPerceptron perceptron;
perceptron.TrainModel(problem);
// 使用测试样本测试
LPerceptronMatrix testSample(1, 2);
testSample[0][0] = -50.0f;
testSample[0][1] = 0.0f;
printf("Predict: %f\n", perceptron.Predict(testSample));
system("pause");
return 0;
}
void ofApp::draw() {
ofBackground(0);
// get grid of 100 x 100 probabilities
for (int i=0; i<100; i++) {
for (int j=0; j<100; j++) {
double x = ofMap(i, 0, 100, 0, ofGetWidth());
double y = ofMap(j, 0, 100, 0, ofGetHeight());
float probability = testSample(x, y);
ofFill();
ofSetColor(ofClamp(ofMap(probability, 0, 0.00001, 0, 255), 0, 255));
ofRect(x, y, ofGetWidth()/100, ofGetHeight()/100);
}
}
// draw random sample on grid
ofSetColor(0, 0, 255);
ofCircle(randSample[0], randSample[1], 10);
ofDrawBitmapString("random sample", randSample[0]+8, randSample[1]);
// get mixture parameters
for (int i=0; i<numGaussians; i++) {
vector<double> mean = gmm.getMean(i);
vector<double> variance = gmm.getVariance(i);
vector<double> std = gmm.getStandardDeviation(i);
double prior = gmm.getPrior(i);
string msg = "Gaussian #"+ofToString(i)+" : prior ("+ofToString(prior)+"), mean ("+ofToString(mean[0])+", "+ofToString(mean[1])+"), standard deviation ("+ofToString(std[0])+", "+ofToString(std[1])+")";
ofSetColor(0, 255, 0);
ofDrawBitmapString(msg, 15, 20 + 20*i);
}
// get sample from mouse
double x = (double) ofGetMouseX();
double y = (double) ofGetMouseY();
float probability = testSample(x, y);
string msg = "P("+ofToString(x) + ", " + ofToString(y) + ") = " + ofToString(probability);
ofDrawBitmapString(msg, x, y);
// message about controls
ofSetColor(255, 0, 0);
ofDrawBitmapString("Press '1' for example of training GMM from data\nPress '2' for example of setting GMM explicitly\nPress spacebar to sample random point from GMM", 20, ofGetHeight()-50);
}
inline NNTYPE NeuralNet::testCycle (Iterator itPatternBegin, Iterator itPatternEnd)
{
m_pSettings->startTestCycle ();
NNTYPE error = 0.0;
NNTYPE sumOfWeights = 0.0;
for (Iterator itPattern = itPatternBegin; itPattern != itPatternEnd; ++itPattern)
{
Pattern& pattern = *itPattern;
error += testSample (pattern.beginInput (), pattern.endInput (), pattern.beginOutput (), pattern.endOutput (), pattern.weight ());
sumOfWeights += pattern.weight ();
}
error /= sumOfWeights;
m_pSettings->endTestCycle ();
return error;
}
