# test_ml.py

# For adaboost.py and entropy.py

# Example use of adaboost.py and entropy.py

# !/usr/bin/python

import numpy as np

import entropy

import adaboost

# these should be 1 if the data point's rmse is lower (by some arbitrary amount) than the baseline rmse and 0 if not

entropy_labels = np.array([1, 1, 0, 0, 1, 0, 1, 0, 0, 1])

# these should be 1 if the data point's rmse is lower and -1 if not

adaboost_labels = np.array([1, 1, -1, -1, 1, -1, 1, -1, -1, 1])

# this is for maxent

# row represents a data point from an experiment

# each column represents a condition

# element (i, j) is 1 if condition j was true during data point i's experiment and 0 if not

data = np.array([[1, 1, 0, 1, 0],

[0, 1, 1, 0, 0]])

# this is for boosting

# element (i, j) is 1 if condition j was true during data point i's experiment and -1 if not

predictions = np.array([[1, 1, -1, 1, -1],

[-1, 1, -1, 1, -1]])

# these give the indices of the top conditions (default is half as many results as there are conditions, rounded up)

# note that maxent doesn't care if the correlation is positive or negative; the top conditions may negatively effect the rmse

entropy_features = entropy.choose_features(data, entropy_labels)

print entropy_features

adaboost_features = adaboost.boost(predictions, adaboost_labels)

print adaboost_features