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文件名称:mahalnobis

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  • 资源属性:
    Windows,,Python,源码
  • 上传时间:
    2017-01-11
  • 文件大小:
    1kb
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介绍说明

z=mahalanobis_classifier(m,S,X).This function outputs the Mahalanobis classifier,

given the mean and covariance matrices.



• M: the number of classes.

• l: the number of features (for each feature vector).

• N: the number of data vectors.

• m: lxM matrix, whose j-th column corresponds to the mean of the j-th class.

• S: lxlxM matrix. S(:,:,j) is the covariance matrix of the j-th normal distribution.

• P: M-dimensional vector whose j-th component is the a priori probability of the j-th class.

• X: lxM data matrix, whose rows are the feature vectors, i.e., data matrix in scikit-learn convention.

• y: N-dimensional vector containing the known class labels, i.e., the ground truth, or target

vector in scikit-learn convention.

• z: N-dimensional vector containing the predicted class labels, i.e., the vector of predicted class

labels in scikit-learn convention.-z=mahalanobis_classifier(m,S,X).This function outputs the Mahalanobis classifier,

given the mean and covariance matrices.



• M: the number of classes.

• l: the number of features (for each feature vector).

• N: the number of data vectors.

• m: lxM matrix, whose j-th column corresponds to the mean of the j-th class.

• S: lxlxM matrix. S(:,:,j) is the covariance matrix of the j-th normal distribution.

• P: M-dimensional vector whose j-th component is the a priori probability of the j-th class.

• X: lxM data matrix, whose rows are the feature vectors, i.e., data matrix in scikit-learn convention.

• y: N-dimensional vector containing the known class labels, i.e., the ground truth, or target

vector in scikit-learn convention.

• z: N-dimensional vector containing the predicted class labels, i.e., the vector of predicted class

labels in scikit-learn convention.
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