/**
* 图象压缩
*
* @throws IOException
*/
public static void part2() throws IOException {
tic();
logger.info("加载数据...\n");
String path = COURSE_ML_PATH + "/ex7/bird_small.png";
MatrixImage mi = ImageLoader.load(path);
FloatMatrix X = mi.getMatrix();
logger.info("模型初始化...\n");
KMeans kMeans = new KMeans(X, 16);
logger.info("执行训练...\n");
FloatMatrix centroids = kMeans.run(10);
logger.info("运行完毕.\n聚类中心如下：\n{}\n", centroids);
logger.info("图象对比...\n");
FloatMatrix indices = kMeans.findClosestCentroids();
int[] index = indices.toIntArray();
FloatMatrix result = centroids.getRows(index);
ImShow.show(mi);
mi.setMatrix(result);
ImShow.show(mi);
toc();
}
@Override
public Object compute(FloatMatrix params, int flag) {
x = params.getRange(0, rows * features);
FloatMatrix theta = params.getRange(rows * features, params.length);
x = x.reshape(rows, features);
theta = theta.reshape(columns, features);
if (flag == 1 || flag == 3) {
FloatMatrix M = MatrixFunctions.pow(x.mmul(theta.transpose()).sub(y), 2);
this.cost = M.mul(r).columnSums().rowSums().get(0) / 2;
if (lambda != 0) {
float cost1 =
(lambda / 2)
* (MatrixFunctions.pow(theta, 2).columnSums().rowSums().get(0)
+ MatrixFunctions.pow(x, 2).columnSums().rowSums().get(0));
this.cost += cost1;
}
}
if (flag == 2 || flag == 3) {
FloatMatrix xGrad = FloatMatrix.zeros(x.rows, x.columns);
FloatMatrix thetaGrad = FloatMatrix.zeros(theta.rows, theta.columns);
int[] indices;
FloatMatrix thetaTemp;
FloatMatrix xTemp;
FloatMatrix yTemp;
for (int i = 0; i < rows; i++) {
indices = r.getRow(i).eq(1).findIndices();
if (indices.length == 0) continue;
thetaTemp = theta.getRows(indices);
yTemp = y.getRow(i).get(indices);
xGrad.putRow(i, x.getRow(i).mmul(thetaTemp.transpose()).sub(yTemp).mmul(thetaTemp));
}
xGrad = xGrad.add(x.mmul(lambda));
for (int i = 0; i < columns; i++) {
indices = r.getColumn(i).eq(1).findIndices();
if (indices.length == 0) continue;
xTemp = x.getRows(indices);
yTemp = y.getColumn(i).get(indices);
thetaGrad.putRow(
i, xTemp.mmul(theta.getRow(i).transpose()).sub(yTemp).transpose().mmul(xTemp));
}
thetaGrad = thetaGrad.add(theta.mmul(lambda));
this.gradient = MatrixUtil.merge(xGrad.data, thetaGrad.data);
}
return flag == 1 ? cost : gradient;
}
public void pmmuli(FloatMatrix self, FloatMatrix other, FloatMatrix result) {
pool.invoke(new MulitplyPartly(self, other, result, 0, self.getRows(), 0, other.getColumns()));
}