Python Machine Learning Framework
Project description
A generic python machine learning framework designed to be flexible and easy to use. It is built upon scikit-learn, numpy, scipy, and some custom written algorithms.
New in version 2.x:
Complete rewrite of main codebase in order to ease in adding new algorithms and much cleaner code
To test it, simply run:
import PyAI
PyAI.test()
The main object in the library is the Brain class (PyAI.Brain). With it you access all of the features in the framework.
brain = PyAI.Brain(x_data=data, y_labels=labels, y_data=reg_data)
| This brain object has 2 modes of operation: classification and regression.
| If you wish to perform classification (discrete) prediction, use the y_labels attribute
| If you wish to perform regression (continuous) prediction, use the y_data attribute
Or you can also provide both
| Then, you must initialize one of the algorithms available by performing:
brain.init_XXX()
# For example
brain.init_clustering(n_clusters=5)
Currently, the available algorithms are
- clustering
- neighbors
- svm
- gmm
- naive_bayes
Then you can apply any number of prediction methods in order to predict using the models
brain.predict_xxx_yyy
# For example
brain.predict_cluster_labels(test_data)
brain.predict_svm_data(test_data)
| The xxx must match on of the algorithms that you have initialized
| The yyy can either be 'labels' or 'data' for classification and regression respectively
New in version 2.x:
Complete rewrite of main codebase in order to ease in adding new algorithms and much cleaner code
To test it, simply run:
import PyAI
PyAI.test()
The main object in the library is the Brain class (PyAI.Brain). With it you access all of the features in the framework.
brain = PyAI.Brain(x_data=data, y_labels=labels, y_data=reg_data)
| This brain object has 2 modes of operation: classification and regression.
| If you wish to perform classification (discrete) prediction, use the y_labels attribute
| If you wish to perform regression (continuous) prediction, use the y_data attribute
Or you can also provide both
| Then, you must initialize one of the algorithms available by performing:
brain.init_XXX()
# For example
brain.init_clustering(n_clusters=5)
Currently, the available algorithms are
- clustering
- neighbors
- svm
- gmm
- naive_bayes
Then you can apply any number of prediction methods in order to predict using the models
brain.predict_xxx_yyy
# For example
brain.predict_cluster_labels(test_data)
brain.predict_svm_data(test_data)
| The xxx must match on of the algorithms that you have initialized
| The yyy can either be 'labels' or 'data' for classification and regression respectively
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