线性回归代码

gistfile1.java

public class LogisticRegression {
  public int N;
	public int n_in;
	public int n_out;
	public double[][] W;
	public double[] b;

	public LogisticRegression(int N, int n_in, int n_out) {
		this.N = N;
		this.n_in = n_in;
		this.n_out = n_out;

		W = new double[this.n_out][this.n_in];
		b = new double[this.n_out];
	}

	public void train(int[] x, int[] y, double lr) {
		double[] p_y_given_x = new double[n_out];
		double[] dy = new double[n_out];

		for (int i = 0; i < n_out; i++) {
			p_y_given_x[i] = 0;
			for (int j = 0; j < n_in; j++) {
				p_y_given_x[i] += W[i][j] * x[j];
			}
			p_y_given_x[i] += b[i];
		}
		softmax(p_y_given_x);

		for (int i = 0; i < n_out; i++) {
			dy[i] = y[i] - p_y_given_x[i];

			for (int j = 0; j < n_in; j++) {
				W[i][j] += lr * dy[i] * x[j] / N;
			}

			b[i] += lr * dy[i] / N;
		}
	}

	public void softmax(double[] x) {
		double max = 0.0;
		double sum = 0.0;

		for (int i = 0; i < n_out; i++)
			if (max < x[i])
				max = x[i];

		for (int i = 0; i < n_out; i++) {
			x[i] = Math.exp(x[i] - max);
			sum += x[i];
		}

		for (int i = 0; i < n_out; i++)
			x[i] /= sum;
	}

	public void predict(int[] x, double[] y) {
		for (int i = 0; i < n_out; i++) {
			y[i] = 0;
			for (int j = 0; j < n_in; j++) {
				y[i] += W[i][j] * x[j];
			}
			y[i] += b[i];
		}

		softmax(y);
	}

	private static void test_lr() {
		double learning_rate = 0.1;
		double n_epochs = 500;

		int train_N = 6;
		int test_N = 2;
		int n_in = 6;
		int n_out = 2;

		int[][] train_X = { { 1, 1, 1, 0, 0, 0 }, { 1, 0, 1, 0, 0, 0 }, { 1, 1, 1, 0, 0, 0 }, { 0, 0, 1, 1, 1, 0 }, { 0, 0, 1, 1, 0, 0 }, { 0, 0, 1, 1, 1, 0 } };

		int[][] train_Y = { { 1, 0 }, { 1, 0 }, { 1, 0 }, { 0, 1 }, { 0, 1 }, { 0, 1 } };

		// construct
		LogisticRegression classifier = new LogisticRegression(train_N, n_in, n_out);

		// train
		for (int epoch = 0; epoch < n_epochs; epoch++) {
			for (int i = 0; i < train_N; i++) {
				classifier.train(train_X[i], train_Y[i], learning_rate);
			}
			// learning_rate *= 0.95;
		}

		// test data
		int[][] test_X = { { 1, 0, 1, 0, 0, 0 }, { 0, 0, 1, 1, 1, 0 } };

		double[][] test_Y = new double[test_N][n_in];

		// test
		for (int i = 0; i < test_N; i++) {
			classifier.predict(test_X[i], test_Y[i]);
			for (int j = 0; j < n_out; j++) {
				System.out.print(test_Y[i][j] + " ");
			}
			System.out.println();
		}
	}

	public static void main(String[] args) {
		test_lr();
	}
}