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decision-tree.js
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decision-tree.js
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var C45, ClassName, Dataset, DecisionTree, Features, ID3, entropy, gain, log2, _;
_ = require("lodash");
Dataset = null;
Features = null;
ClassName = null;
DecisionTree = function(_Dataset, _Features, _ClassName, _Options) {
var BinarySplits;
if (_Options == null) {
_Options = {};
}
Dataset = _Dataset;
Features = _Features;
ClassName = _ClassName;
BinarySplits = _Options.BinarySplits || false;
console.log(entropy(Dataset, ClassName));
if (BinarySplits) {
ID3();
} else {
C45();
}
};
ID3 = function() {};
C45 = function() {};
gain = function() {};
entropy = function(Values, Class) {
var Entropy, Proportion, UniqueValues, Value, _i, _len, _ref;
Entropy = 0;
UniqueValues = _.countBy(Values, function(Entry) {
return Entry[Class];
});
_ref = Object.keys(UniqueValues);
for (_i = 0, _len = _ref.length; _i < _len; _i++) {
Value = _ref[_i];
Proportion = UniqueValues[Value] / Values.length;
Entropy += Proportion * log2(Proportion);
}
return -Entropy;
};
log2 = function(x) {
return Math.log(x) / Math.log(2);
};
module.exports = DecisionTree;