68 lines
2.0 KiB
C
68 lines
2.0 KiB
C
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/***************************************************************************
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Module Name:
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Gaussian Mixture Model with Diagonal Covariance Matrix
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History:
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2003/11/01 Fei Wang
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2013 luxiaoxun
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***************************************************************************/
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#pragma once
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#include <fstream>
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#define M_PI 3.1415926535897932384626433832795
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class GMM
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{
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public:
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GMM(int dimNum = 1, int mixNum = 1);
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~GMM();
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void Copy(GMM* gmm);
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void SetMaxIterNum(int i) { m_maxIterNum = i; }
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void SetEndError(double f) { m_endError = f; }
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int GetDimNum() { return m_dimNum; }
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int GetMixNum() { return m_mixNum; }
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int GetMaxIterNum() { return m_maxIterNum; }
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double GetEndError() { return m_endError; }
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double& Prior(int i) { return m_priors[i]; }
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double* Mean(int i) { return m_means[i]; }
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double* Variance(int i) { return m_vars[i]; }
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void setPrior(int i, double val) { m_priors[i] = val; }
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void setMean(int i, double* val) { for (int j = 0;j < m_dimNum;j++) m_means[i][j] = val[j]; }
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void setVariance(int i, double* val) { for (int j = 0;j < m_dimNum;j++) m_vars[i][j] = val[j]; }
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double GetProbability(const double* sample);
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/* SampleFile: <size><dim><data>...*/
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void Init(const char* sampleFileName);
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void Train(const char* sampleFileName);
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void Init(double* data, int N);
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void Train(double* data, int N);
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void DumpSampleFile(const char* fileName);
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friend std::ostream& operator<<(std::ostream& out, GMM& gmm);
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friend std::istream& operator>>(std::istream& in, GMM& gmm);
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private:
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int m_dimNum; // 样本维数
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int m_mixNum; // Gaussian数目
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double* m_priors; // Gaussian权重
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double** m_means; // Gaussian均值
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double** m_vars; // Gaussian方差
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// A minimum variance is required. Now, it is the overall variance * 0.01.
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double* m_minVars;
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int m_maxIterNum; // The stopping criterion regarding the number of iterations
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double m_endError; // The stopping criterion regarding the error
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private:
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// Return the "j"th pdf, p(x|j).
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double GetProbability(const double* x, int j);
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void Allocate();
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void Dispose();
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};
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