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dml's Introduction

iDML

D's Machine Learning is a machine learning toolkit for python,focus on rightness but efficiency

all code is based on numpy and scipy


Code Files

./dml/NN -the code of Neural NetWorks

./dml/LR -Logistic Regression,actually It's softmax

./dml/DT -Decision Tree , CART algorithm

./dml/ClUSTER -some cluster algorithm,inculde kmeans \ kmedoids \ spectralCluster \ Hierarchical Cluster

./dml/ADAB -the adaboost algorithm

./dml/KNN -the k-Nearest Neighbor algorithm(kd-tree BBF implementing)

./dml/NB -the naive Bayesian support both continuous and descrete features

./dml/SVM -the basic binary Support Vector Machine

./dml/CNN -the simple Convolutional Neural Networks

./dml/CF -some Collaborative Filtering Algorithm implement,include item-based \ SVD \ RBM

./dml/tool -include some basic tools for computing

./test/ -include some test code for DML


Class Format

all class can be used in this way:(LR for example)

but there is still some different Initialization parameters in different class,also the predict function

sorry for this but most class use pred() and NN use nnpred(),I may formalize them in the future

a = LRC(train_images,trian_labels,nor=False)
a.train(200,True)
pred = a.predict(test_images)

for the input X and y ,X must be a N*M matrix and y is a vector length M

where N is the #feature and M is #training_case

for the cluster method,you can use a.labels or a.result() to get the final result


Install

DML is based on numpy,scipy,matplotlib .you should install them first

This packages uses setuptools, which is the default way of installing python modules. The install command is:(sudo is required in some system)

python setup.py build
python setup.py install

Warning

  • only python 2 is supported,sorry for the python 3 user.

  • some method from numpy and scipy will report warning because of their version


License

WTFPL

dml's People

Contributors

justdark avatar timgates42 avatar

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dml's Issues

Proposing a PR to fix a few small typos

Issue Type

[x] Bug (Typo)

Steps to Replicate and Expected Behaviour

  • Examine README.md, dml/DT/decisionTree.py, dml/NB/naiveBayesian.py and observe continous, however expect to see continuous.
  • Examine dml/NB/naiveBayesian.py, test/naiveBayesian/nb_test.py and observe descret, however expect to see discreet.
  • Examine README.md, dml/LR/logisticRegression.py and observe actualy, however expect to see actually.
  • Examine dml/CNN/CNN.py and observe refered, however expect to see referred.
  • Examine dml/NN/neuralNetwork.py and observe recommand, however expect to see recommend.

Notes

Semi-automated issue generated by
https://github.com/timgates42/meticulous/blob/master/docs/NOTE.md

To avoid wasting CI processing resources a branch with the fix has been
prepared but a pull request has not yet been created. A pull request fixing
the issue can be prepared from the link below, feel free to create it or
request @timgates42 create the PR. Alternatively if the fix is undesired please
close the issue with a small comment about the reasoning.

https://github.com/timgates42/dml/pull/new/bugfix_typos

Thanks.

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