数据集seeds.tsv
15.26 14.84 0.871 5.763 3.312 2.221 5.22 Kama 14.88 14.57 0.8811 5.554 3.333 1.018 4.956 Kama 14.29 14.09 0.905 5.291 3.337 2.699 4.825 Kama 13.84 13.94 0.8955 5.324 3.379 2.259 4.805 Kama 16.14 14.99 0.9034 5.658 3.562 1.355 5.175 Kama 14.38 14.21 0.8951 5.386 3.312 2.462 4.956 Kama 14.69 14.49 0.8799 5.563 3.259 3.586 5.219 Kama 14.11 14.1 0.8911 5.42 3.302 2.7 5.0 Kama 16.63 15.46 0.8747 6.053 3.465 2.04 5.877 Kama 16.44 15.25 0.888 5.884 3.505 1.969 5.533 Kama 15.26 14.85 0.8696 5.714 3.242 4.543 5.314 Kama 14.03 14.16 0.8796 5.438 3.201 1.717 5.001 Kama 13.89 14.02 0.888 5.439 3.199 3.986 4.738 Kama 13.78 14.06 0.8759 5.479 3.156 3.136 4.872 Kama 13.74 14.05 0.8744 5.482 3.114 2.932 4.825 Kama 14.59 14.28 0.8993 5.351 3.333 4.185 4.781 Kama 13.99 13.83 0.9183 5.119 3.383 5.234 4.781 Kama 15.69 14.75 0.9058 5.527 3.514 1.599 5.046 Kama 14.7 14.21 0.9153 5.205 3.466 1.767 4.649 Kama 12.72 13.57 0.8686 5.226 3.049 4.102 4.914 Kama 14.16 14.4 0.8584 5.658 3.129 3.072 5.176 Kama 14.11 14.26 0.8722 5.52 3.168 2.688 5.219 Kama 15.88 14.9 0.8988 5.618 3.507 0.7651 5.091 Kama 12.08 13.23 0.8664 5.099 2.936 1.415 4.961 Kama 15.01 14.76 0.8657 5.789 3.245 1.791 5.001 Kama 16.19 15.16 0.8849 5.833 3.421 0.903 5.307 Kama 13.02 13.76 0.8641 5.395 3.026 3.373 4.825 Kama 12.74 13.67 0.8564 5.395 2.956 2.504 4.869 Kama 14.11 14.18 0.882 5.541 3.221 2.754 5.038 Kama 13.45 14.02 0.8604 5.516 3.065 3.531 5.097 Kama 13.16 13.82 0.8662 5.454 2.975 0.8551 5.056 Kama 15.49 14.94 0.8724 5.757 3.371 3.412 5.228 Kama 14.09 14.41 0.8529 5.717 3.186 3.92 5.299 Kama 13.94 14.17 0.8728 5.585 3.15 2.124 5.012 Kama 15.05 14.68 0.8779 5.712 3.328 2.129 5.36 Kama 16.12 15.0 0.9 5.709 3.485 2.27 5.443 Kama 16.2 15.27 0.8734 5.826 3.464 2.823 5.527 Kama 17.08 15.38 0.9079 5.832 3.683 2.956 5.484 Kama 14.8 14.52 0.8823 5.656 3.288 3.112 5.309 Kama 14.28 14.17 0.8944 5.397 3.298 6.685 5.001 Kama 13.54 13.85 0.8871 5.348 3.156 2.587 5.178 Kama 13.5 13.85 0.8852 5.351 3.158 2.249 5.176 Kama 13.16 13.55 0.9009 5.138 3.201 2.461 4.783 Kama 15.5 14.86 0.882 5.877 3.396 4.711 5.528 Kama 15.11 14.54 0.8986 5.579 3.462 3.128 5.18 Kama 13.8 14.04 0.8794 5.376 3.155 1.56 4.961 Kama 15.36 14.76 0.8861 5.701 3.393 1.367 5.132 Kama 14.99 14.56 0.8883 5.57 3.377 2.958 5.175 Kama 14.79 14.52 0.8819 5.545 3.291 2.704 5.111 Kama 14.86 14.67 0.8676 5.678 3.258 2.129 5.351 Kama 14.43 14.4 0.8751 5.585 3.272 3.975 5.144 Kama 15.78 14.91 0.8923 5.674 3.434 5.593 5.136 Kama 14.49 14.61 0.8538 5.715 3.113 4.116 5.396 Kama 14.33 14.28 0.8831 5.504 3.199 3.328 5.224 Kama 14.52 14.6 0.8557 5.741 3.113 1.481 5.487 Kama 15.03 14.77 0.8658 5.702 3.212 1.933 5.439 Kama 14.46 14.35 0.8818 5.388 3.377 2.802 5.044 Kama 14.92 14.43 0.9006 5.384 3.412 1.142 5.088 Kama 15.38 14.77 0.8857 5.662 3.419 1.999 5.222 Kama 12.11 13.47 0.8392 5.159 3.032 1.502 4.519 Kama 11.42 12.86 0.8683 5.008 2.85 2.7 4.607 Kama 11.23 12.63 0.884 4.902 2.879 2.269 4.703 Kama 12.36 13.19 0.8923 5.076 3.042 3.22 4.605 Kama 13.22 13.84 0.868 5.395 3.07 4.157 5.088 Kama 12.78 13.57 0.8716 5.262 3.026 1.176 4.782 Kama 12.88 13.5 0.8879 5.139 3.119 2.352 4.607 Kama 14.34 14.37 0.8726 5.63 3.19 1.313 5.15 Kama 14.01 14.29 0.8625 5.609 3.158 2.217 5.132 Kama 14.37 14.39 0.8726 5.569 3.153 1.464 5.3 Kama 12.73 13.75 0.8458 5.412 2.882 3.533 5.067 Kama 17.63 15.98 0.8673 6.191 3.561 4.076 6.06 Rosa 16.84 15.67 0.8623 5.998 3.484 4.675 5.877 Rosa 17.26 15.73 0.8763 5.978 3.594 4.539 5.791 Rosa 19.11 16.26 0.9081 6.154 3.93 2.936 6.079 Rosa 16.82 15.51 0.8786 6.017 3.486 4.004 5.841 Rosa 16.77 15.62 0.8638 5.927 3.438 4.92 5.795 Rosa 17.32 15.91 0.8599 6.064 3.403 3.824 5.922 Rosa 20.71 17.23 0.8763 6.579 3.814 4.451 6.451 Rosa 18.94 16.49 0.875 6.445 3.639 5.064 6.362 Rosa 17.12 15.55 0.8892 5.85 3.566 2.858 5.746 Rosa 16.53 15.34 0.8823 5.875 3.467 5.532 5.88 Rosa 18.72 16.19 0.8977 6.006 3.857 5.324 5.879 Rosa 20.2 16.89 0.8894 6.285 3.864 5.173 6.187 Rosa 19.57 16.74 0.8779 6.384 3.772 1.472 6.273 Rosa 19.51 16.71 0.878 6.366 3.801 2.962 6.185 Rosa 18.27 16.09 0.887 6.173 3.651 2.443 6.197 Rosa 18.88 16.26 0.8969 6.084 3.764 1.649 6.109 Rosa 18.98 16.66 0.859 6.549 3.67 3.691 6.498 Rosa 21.18 17.21 0.8989 6.573 4.033 5.78 6.231 Rosa 20.88 17.05 0.9031 6.45 4.032 5.016 6.321 Rosa 20.1 16.99 0.8746 6.581 3.785 1.955 6.449 Rosa 18.76 16.2 0.8984 6.172 3.796 3.12 6.053 Rosa 18.81 16.29 0.8906 6.272 3.693 3.237 6.053 Rosa 18.59 16.05 0.9066 6.037 3.86 6.001 5.877 Rosa 18.36 16.52 0.8452 6.666 3.485 4.933 6.448 Rosa 16.87 15.65 0.8648 6.139 3.463 3.696 5.967 Rosa 19.31 16.59 0.8815 6.341 3.81 3.477 6.238 Rosa 18.98 16.57 0.8687 6.449 3.552 2.144 6.453 Rosa 18.17 16.26 0.8637 6.271 3.512 2.853 6.273 Rosa 18.72 16.34 0.881 6.219 3.684 2.188 6.097 Rosa 16.41 15.25 0.8866 5.718 3.525 4.217 5.618 Rosa 17.99 15.86 0.8992 5.89 3.694 2.068 5.837 Rosa 19.46 16.5 0.8985 6.113 3.892 4.308 6.009 Rosa 19.18 16.63 0.8717 6.369 3.681 3.357 6.229 Rosa 18.95 16.42 0.8829 6.248 3.755 3.368 6.148 Rosa 18.83 16.29 0.8917 6.037 3.786 2.553 5.879 Rosa 18.85 16.17 0.9056 6.152 3.806 2.843 6.2 Rosa 17.63 15.86 0.88 6.033 3.573 3.747 5.929 Rosa 19.94 16.92 0.8752 6.675 3.763 3.252 6.55 Rosa 18.55 16.22 0.8865 6.153 3.674 1.738 5.894 Rosa 18.45 16.12 0.8921 6.107 3.769 2.235 5.794 Rosa 19.38 16.72 0.8716 6.303 3.791 3.678 5.965 Rosa 19.13 16.31 0.9035 6.183 3.902 2.109 5.924 Rosa 19.14 16.61 0.8722 6.259 3.737 6.682 6.053 Rosa 20.97 17.25 0.8859 6.563 3.991 4.677 6.316 Rosa 19.06 16.45 0.8854 6.416 3.719 2.248 6.163 Rosa 18.96 16.2 0.9077 6.051 3.897 4.334 5.75 Rosa 19.15 16.45 0.889 6.245 3.815 3.084 6.185 Rosa 18.89 16.23 0.9008 6.227 3.769 3.639 5.966 Rosa 20.03 16.9 0.8811 6.493 3.857 3.063 6.32 Rosa 20.24 16.91 0.8897 6.315 3.962 5.901 6.188 Rosa 18.14 16.12 0.8772 6.059 3.563 3.619 6.011 Rosa 16.17 15.38 0.8588 5.762 3.387 4.286 5.703 Rosa 18.43 15.97 0.9077 5.98 3.771 2.984 5.905 Rosa 15.99 14.89 0.9064 5.363 3.582 3.336 5.144 Rosa 18.75 16.18 0.8999 6.111 3.869 4.188 5.992 Rosa 18.65 16.41 0.8698 6.285 3.594 4.391 6.102 Rosa 17.98 15.85 0.8993 5.979 3.687 2.257 5.919 Rosa 20.16 17.03 0.8735 6.513 3.773 1.91 6.185 Rosa 17.55 15.66 0.8991 5.791 3.69 5.366 5.661 Rosa 18.3 15.89 0.9108 5.979 3.755 2.837 5.962 Rosa 18.94 16.32 0.8942 6.144 3.825 2.908 5.949 Rosa 15.38 14.9 0.8706 5.884 3.268 4.462 5.795 Rosa 16.16 15.33 0.8644 5.845 3.395 4.266 5.795 Rosa 15.56 14.89 0.8823 5.776 3.408 4.972 5.847 Rosa 15.38 14.66 0.899 5.477 3.465 3.6 5.439 Rosa 17.36 15.76 0.8785 6.145 3.574 3.526 5.971 Rosa 15.57 15.15 0.8527 5.92 3.231 2.64 5.879 Rosa 15.6 15.11 0.858 5.832 3.286 2.725 5.752 Rosa 16.23 15.18 0.885 5.872 3.472 3.769 5.922 Rosa 13.07 13.92 0.848 5.472 2.994 5.304 5.395 Canadian 13.32 13.94 0.8613 5.541 3.073 7.035 5.44 Canadian 13.34 13.95 0.862 5.389 3.074 5.995 5.307 Canadian 12.22 13.32 0.8652 5.224 2.967 5.469 5.221 Canadian 11.82 13.4 0.8274 5.314 2.777 4.471 5.178 Canadian 11.21 13.13 0.8167 5.279 2.687 6.169 5.275 Canadian 11.43 13.13 0.8335 5.176 2.719 2.221 5.132 Canadian 12.49 13.46 0.8658 5.267 2.967 4.421 5.002 Canadian 12.7 13.71 0.8491 5.386 2.911 3.26 5.316 Canadian 10.79 12.93 0.8107 5.317 2.648 5.462 5.194 Canadian 11.83 13.23 0.8496 5.263 2.84 5.195 5.307 Canadian 12.01 13.52 0.8249 5.405 2.776 6.992 5.27 Canadian 12.26 13.6 0.8333 5.408 2.833 4.756 5.36 Canadian 11.18 13.04 0.8266 5.22 2.693 3.332 5.001 Canadian 11.36 13.05 0.8382 5.175 2.755 4.048 5.263 Canadian 11.19 13.05 0.8253 5.25 2.675 5.813 5.219 Canadian 11.34 12.87 0.8596 5.053 2.849 3.347 5.003 Canadian 12.13 13.73 0.8081 5.394 2.745 4.825 5.22 Canadian 11.75 13.52 0.8082 5.444 2.678 4.378 5.31 Canadian 11.49 13.22 0.8263 5.304 2.695 5.388 5.31 Canadian 12.54 13.67 0.8425 5.451 2.879 3.082 5.491 Canadian 12.02 13.33 0.8503 5.35 2.81 4.271 5.308 Canadian 12.05 13.41 0.8416 5.267 2.847 4.988 5.046 Canadian 12.55 13.57 0.8558 5.333 2.968 4.419 5.176 Canadian 11.14 12.79 0.8558 5.011 2.794 6.388 5.049 Canadian 12.1 13.15 0.8793 5.105 2.941 2.201 5.056 Canadian 12.44 13.59 0.8462 5.319 2.897 4.924 5.27 Canadian 12.15 13.45 0.8443 5.417 2.837 3.638 5.338 Canadian 11.35 13.12 0.8291 5.176 2.668 4.337 5.132 Canadian 11.24 13.0 0.8359 5.09 2.715 3.521 5.088 Canadian 11.02 13.0 0.8189 5.325 2.701 6.735 5.163 Canadian 11.55 13.1 0.8455 5.167 2.845 6.715 4.956 Canadian 11.27 12.97 0.8419 5.088 2.763 4.309 5.0 Canadian 11.4 13.08 0.8375 5.136 2.763 5.588 5.089 Canadian 10.83 12.96 0.8099 5.278 2.641 5.182 5.185 Canadian 10.8 12.57 0.859 4.981 2.821 4.773 5.063 Canadian 11.26 13.01 0.8355 5.186 2.71 5.335 5.092 Canadian 10.74 12.73 0.8329 5.145 2.642 4.702 4.963 Canadian 11.48 13.05 0.8473 5.18 2.758 5.876 5.002 Canadian 12.21 13.47 0.8453 5.357 2.893 1.661 5.178 Canadian 11.41 12.95 0.856 5.09 2.775 4.957 4.825 Canadian 12.46 13.41 0.8706 5.236 3.017 4.987 5.147 Canadian 12.19 13.36 0.8579 5.24 2.909 4.857 5.158 Canadian 11.65 13.07 0.8575 5.108 2.85 5.209 5.135 Canadian 12.89 13.77 0.8541 5.495 3.026 6.185 5.316 Canadian 11.56 13.31 0.8198 5.363 2.683 4.062 5.182 Canadian 11.81 13.45 0.8198 5.413 2.716 4.898 5.352 Canadian 10.91 12.8 0.8372 5.088 2.675 4.179 4.956 Canadian 11.23 12.82 0.8594 5.089 2.821 7.524 4.957 Canadian 10.59 12.41 0.8648 4.899 2.787 4.975 4.794 Canadian 10.93 12.8 0.839 5.046 2.717 5.398 5.045 Canadian 11.27 12.86 0.8563 5.091 2.804 3.985 5.001 Canadian 11.87 13.02 0.8795 5.132 2.953 3.597 5.132 Canadian 10.82 12.83 0.8256 5.18 2.63 4.853 5.089 Canadian 12.11 13.27 0.8639 5.236 2.975 4.132 5.012 Canadian 12.8 13.47 0.886 5.16 3.126 4.873 4.914 Canadian 12.79 13.53 0.8786 5.224 3.054 5.483 4.958 Canadian 13.37 13.78 0.8849 5.32 3.128 4.67 5.091 Canadian 12.62 13.67 0.8481 5.41 2.911 3.306 5.231 Canadian 12.76 13.38 0.8964 5.073 3.155 2.828 4.83 Canadian 12.38 13.44 0.8609 5.219 2.989 5.472 5.045 Canadian 12.67 13.32 0.8977 4.984 3.135 2.3 4.745 Canadian 11.18 12.72 0.868 5.009 2.81 4.051 4.828 Canadian 12.7 13.41 0.8874 5.183 3.091 8.456 5.0 Canadian 12.37 13.47 0.8567 5.204 2.96 3.919 5.001 Canadian 12.19 13.2 0.8783 5.137 2.981 3.631 4.87 Canadian 11.23 12.88 0.8511 5.14 2.795 4.325 5.003 Canadian 13.2 13.66 0.8883 5.236 3.232 8.315 5.056 Canadian 11.84 13.21 0.8521 5.175 2.836 3.598 5.044 Canadian 12.3 13.34 0.8684 5.243 2.974 5.637 5.063 Canadian
最近邻算法knn.py
#coding:utf-8 import numpy as np def learn_model(k, features, labels): return k, features.copy(),labels.copy() def plurality(xs):#xs最近邻的标签 from collections import defaultdict #支持缺省值的集合子类 counts = defaultdict(int) #给字典value元素添加默认类型 int for x in xs: counts[x] += 1 #对 xs 中的元素进行计数 maxv = max(counts.values()) #返回出现次数最多的标签 for k,v in counts.items(): if v == maxv: return k def apply_model(features, model):#训练集、训练标签 k, train_feats, labels = model#k, features.copy(),labels.copy() results = [] for f in features: label_dist = [] #用来存(k维最近邻,标签)元组 for t,ell in zip(train_feats, labels): label_dist.append( (np.linalg.norm(f-t), ell) )#(norm(f-t)n维的距离、标签) norm:向量标准化 label_dist.sort(key=lambda d_ell: d_ell[0])#按元祖第一个元素排序 label_dist = label_dist[:k] #取前 k (k = 1) 个数值 results.append(plurality([ell for _,ell in label_dist]))# _ 无实际意义 return np.array(results) #返回预测标签数组 def accuracy(features, labels, model): preds = apply_model(features, model) return np.mean(preds == labels)
主函数seeds_knn.py
#coding:utf-8 from load import load_dataset import numpy as np from knn import learn_model, apply_model, accuracy features,labels = load_dataset('seeds') #数据特征、标签 def cross_validate(features, labels): error = 0.0 for fold in range(10): #十折交叉验证 training = np.ones(len(features), bool) training[fold::10] = 0 testing = ~training model = learn_model(1, features[training], labels[training])#k = 1 test_error = accuracy(features[testing], labels[testing], model)#预测值与真实值比较 error += test_error return error/ 10.0 #取十次的平均 error = cross_validate(features, labels) #数据集、标签 print('Ten fold cross-validated error was {0:.1%}.'.format(error)) features -= features.mean(0) #转化为无量纲 从特征值中减去特征的平均值 features /= features.std(0) #将特征值除以它的标准差 error = cross_validate(features, labels) print('Ten fold cross-validated error after z-scoring was {0:.1%}.'.format(error))
load.py
import numpy as np def load_dataset(dataset_name): data = [] label = [] with open('{0}.tsv'.format(dataset_name),'r') as f: lines = f.readlines() for line in lines: linedata = line.strip().split(' ') data.append([float(da) for da in linedata[:-1]])# 从第0列到倒数第一列 label.append(linedata[-1]) data = np.array(data) label = np.array(label) return data,label
k分别取不同值时的结果:
