numpy-使用numpy实现的机器学习算法之KMeans.zip

import os import sys root_path = os.path.abspath("../") if root_path not in sys.path: sys.path.append(root_path) import numpy as np from Util.Util import DataUtil from Util.Bases import ClassifierBase from Util.ProgressBar import ProgressBar class KMeans(ClassifierBase): def __init__(self, **kwargs): super(KMeans, self).__init__(**kwargs) self._centers = self._counter = None self._params["n_clusters"] = kwargs.get("n_clusters", 2) self._params["epoch"] = kwargs.get("epoch", 1000) self._params["norm"] = kwargs.get("norm", "l2") def fit(self, x, n_clusters=None, epoch=None, norm=None, animation_params=None): if n_clusters is None: n_clusters = self._params["n_clusters"] if epoch is None: epoch = self._params["epoch"] if norm is not None: self._params["norm"] = norm *animation_properties, animation_params = self._get_animation_params(animation_params) x = np.atleast_2d(x) arange = np.arange(n_clusters)[..., None] x_high_dim, labels_cache, counter = x[:, None, ...], None, 0 self._centers = x[np.random.permutation(len(x))[:n_clusters]] bar = ProgressBar(max_value=epoch, name="KMeans") ims = [] for i in range(epoch): labels = self.predict(x_high_dim, high_dim=True) if labels_cache is None: labels_cache = labels else: if np.all(labels_cache == labels): bar.update(epoch) break else: labels_cache = labels for j, indices in enumerate(labels == arange): self._centers[j] = np.average(x[indices], axis=0) counter += 1 animation_params["extra"] = self._centers self._handle_animation(i, x, labels, ims, animation_params, *animation_properties) bar.update() self._counter = counter self._handle_mp4(ims, animation_properties) def predict(self, x, get_raw_results=False, high_dim=False): if not high_dim: x = x[:, None, ...] dis = np.abs(x - self._centers) if self._params["norm"] == "l1" else (x - self._centers) ** 2 return np.argmin(np.sum(dis, axis=2), axis=1) if __name__ == '__main__': _x, _y = DataUtil.gen_random(size=2000, scale=6) k_means = KMeans(n_clusters=8, animation_params={ "show": False, "mp4": True, "period": 1, "draw_background": True }) k_means.fit(_x) k_means.visualize2d(_x, _y, dense=400, extra=k_means["centers"]) _x, _y = DataUtil.gen_two_clusters() k_means = KMeans() k_means.fit(_x) k_means.visualize2d(_x, _y, dense=400, extra=k_means["centers"]) _x, _y = DataUtil.gen_two_clusters(n_dim=3) k_means = KMeans() k_means.fit(_x) k_means.visualize3d(_x, _y, dense=100, extra=k_means["centers"])