mlpy机器学习库的介绍

mlpy机器学习库的介绍

1、Introduce

mlpy is a Python module for Machine Learning built on top of NumPy/SciPy and the GNU Scientific Libraries.

mlpy provides a wide range of state-of-the-art machine learning methods for supervised and unsupervised problems and it is aimed at finding a reasonable compromise among modularity, maintainability, reproducibility, usability and efficiency. mlpy is multiplatform, it works with Python 2 and 3 and it is Open Source, distributed under the GNU General Public License version 3.

2、Documentation

(1)online Documentation:http://mlpy.sourceforge.net/docs/3.5/

(2)PDF Documentation:https://sourceforge.net/projects/mlpy/files/mlpy%203.5.0/

3、CONTENTS

2 Introduction 2.1 Conventions 3 Tutorial 3.1 Tutorial 1 - Iris Dataset 4 Linear Methods for Regression 4.1 Ordinary Least Squares 4.2 Ridge Regression 4.3 Partial Least Squares 4.4 Last Angle Regression (LARS) 4.5 Elastic Net 5 Linear Methods for Classification 5.1 Linear Discriminant Analysis Classifier (LDAC) 5.2 Basic Perceptron 5.3 Elastic Net Classifier 5.4 Logistic Regression5.5 Support Vector Classification 5.6 Diagonal Linear Discriminant Analysis (DLDA) 5.7 Golub Classifier 

6 Kernels 6.1 Kernel Functions 6.2 Kernel Classes 6.3 Functions 6.4 Centering in Feature Space 6.5 Make a Custom Kernel 7 Non Linear Methods for Regression 7.1 Kernel Ridge Regression 7.2 Support Vector Regression 8 Non Linear Methods for Classification 8.1 Parzen-based classifier8.2 Support Vector Classification 8.3 Kernel Fisher Discriminant Classifier 8.4 k-Nearest-Neighbor 8.5 Classification Tree 8.6 Maximum Likelihood Classifier 9 Support Vector Machines (SVMs) 9.1 Support Vector Machines from [LIBSVM]9.2 Kernel Adatron 10 Large Linear Classification from [LIBLINEAR] 

11 Cluster Analysis 11.1 Hierarchical Clustering 11.2 Memory-saving Hierarchical Clustering11.3 k-means 12 Algorithms for Feature Weighting 12.1 Iterative RELIEF13 Feature Selection 13.1 Recursive Feature Elimination14 Dimensionality Reduction 14.1 Linear Discriminant Analysis (LDA) 14.2 Spectral Regression Discriminant Analysis (SRDA) 14.3 Kernel Fisher Discriminant Analysis (KFDA)14.4 Principal Component Analysis (PCA) 14.5 Fast Principal Component Analysis (PCAFast)14.6 Kernel Principal Component Analysis (KPCA)15 Cross Validation 15.1 Leave-one-out and k-fold15.2 Random Subsampling (aka MonteCarlo)15.3 All Combinations16 Metrics 16.1 Classification 16.2 Regression17 A Set of Statistical Functions 

18 Canberra Distances and Stability Indicator of Ranked Lists 18.1 Canberra distance 18.2 Canberra Distance with Location Parameter 18.3 Canberra Stability Indicator19 Borda Count 

20 Find Peaks 

21 Dynamic Time Warping (DTW) 21.1 Standard DTW 21.2 Subsequence DTW 22 Longest Common Subsequence (LCS) 22.1 Standard LCS 22.2 LCS for real series23 mlpy.wavelet - Wavelet Transform 23.1 Padding 23.2 Discrete Wavelet Transform23.3 Undecimated Wavelet Transform23.4 Continuous Wavelet Transform 24 Short Guide to Centering and Scaling