计算质心和精度

Question

我有两个点feat_left, feat_right获得从连体网络和我绘制这些点在x,y坐标如下所示。

这里是python脚本

import json 
import matplotlib.pyplot as plt 
import numpy as np 



data = json.load(open('predictions-mnist.txt')) 

n=len(data['outputs'].items()) 
label_list = np.array(range(n)) 
feat_left = np.random.random((n,2)) 


count=1 

for key,val in data['outputs'].items(): 
    feat = data['outputs'][key]['feat_left'] 
    feat_left[count-1] = feat 
    key = key.split("/") 
    key = int(key[6]) 
    label_list[count - 1] = key 
    count = count + 1 


f = plt.figure(figsize=(16,9)) 

c = ['#ff0000', '#ffff00', '#00ff00', '#00ffff', '#0000ff', 
    '#ff00ff', '#990000', '#999900', '#009900', '#009999'] 

for i in range(10): 
    plt.plot(feat_left[label_list==i,0].flatten(), feat_left[label_list==i,1].flatten(), '.', c=c[i]) 
plt.legend(['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']) 
plt.grid() 
plt.show() 

现在我想计算质心然后纯度每个集群的。

Answer 1

重心仅仅是mean：

centorids = np.zeros((10,2), dtype='f4') 
for i in xrange(10): 
    centroids[i,:] = np.mean(feat_left[label_list==i, :2], axis=0) 

至于精度，可以计算从质心的均方误差（距离）：

sqerr = np.zeros((10,), dtype='f4') 
for i in xrange(10): 
    sqerr[i] = np.sum((feat_left[label_list==i, :2]-centroids[i,:])**2) 

计算purity：

def compute_cluster_purity(gt_labels, pred_labels): 
    """ 
    Compute purity of predicted labels (pred_labels), given 
    the ground-truth labels (gt_labels). 

    Assuming gt_labels and pred_labels are both lists of int of length n 
    """ 
    n = len(gt_labels) # number of elements 
    assert len(pred_labels) == n 
    purity = 0 
    for l in set(pred_labels): 
    # for predicted label l, what are the gt_labels of this cluster? 
    gt = [gt_labels[i] for i, il in enumerate(pred_labels) if il==l] 
    # most frequent gt label in this cluster: 
    mfgt = max(set(gt), key=gt.count) 
    purity += gt.count(mfgt) # count intersection between most frequent ground truth and this cluster 
    return float(purity)/n 

请参阅this answer了解sele的更多详情设置群集中最频繁的标签。