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54 lines
1.5 KiB
Markdown
54 lines
1.5 KiB
Markdown
# LeastSquares Linear Regression
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Linear model that uses least squares method to approximate solution.
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### Train
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To train a model, simply provide train samples and targets values (as `array`). Example:
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```
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$samples = [[60], [61], [62], [63], [65]];
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$targets = [3.1, 3.6, 3.8, 4, 4.1];
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$regression = new LeastSquares();
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$regression->train($samples, $targets);
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```
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You can train the model using multiple data sets, predictions will be based on all the training data.
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### Predict
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To predict sample target value, use the `predict` method with sample to check (as `array`). Example:
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```
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$regression->predict([64]);
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// return 4.06
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```
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### Multiple Linear Regression
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The term multiple attached to linear regression means that there are two or more sample parameters used to predict target.
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For example you can use: mileage and production year to predict the price of a car.
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```
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$samples = [[73676, 1996], [77006, 1998], [10565, 2000], [146088, 1995], [15000, 2001], [65940, 2000], [9300, 2000], [93739, 1996], [153260, 1994], [17764, 2002], [57000, 1998], [15000, 2000]];
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$targets = [2000, 2750, 15500, 960, 4400, 8800, 7100, 2550, 1025, 5900, 4600, 4400];
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$regression = new LeastSquares();
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$regression->train($samples, $targets);
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$regression->predict([60000, 1996])
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// return 4094.82
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```
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### Intercept and Coefficients
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After you train your model, you can get the intercept and coefficients array.
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```
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$regression->getIntercept();
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// return -7.9635135135131
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$regression->getCoefficients();
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// return [array(1) {[0]=>float(0.18783783783783)}]
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```
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