模板匹配法说白就是特征一一对应,将数据每个特征相差加起来,然后总的特征值最小的就是相似度最大的
关于这里的数据集用的不是书上配套的,具体请看本专栏第一篇内容
import numpy as np
import random
def train_test_split(x,y,ratio = 3):
"""
:function: 对数据集划分为训练集、测试集
:param x: m*n维 m表示数据个数 n表示特征个数
:param y: 标签
:param ratio: 产生比例 train:test = 3:1(默认比例)
:return: x_train y_train x_test y_test
"""
n_samples , n_train = x.shape[0] , int(x.shape[0]*(ratio)/(1+ratio))
train_id = random.sample(range(0,n_samples),n_train)
x_train = x[train_id,:]
y_train = y[train_id]
x_test = np.delete(x,train_id,axis = 0)
y_test = np.delete(y,train_id,axis = 0)
return x_train,y_train,x_test,y_test
def neartemplet(x_train,y_train,sample):
"""
:function: 模板匹配法
:param X_train: 训练集 M*N M为样本个数 N为特征个数
:param y_train: 训练集标签 1*M
:param sample: 待识别样品
:return: 返回判断类别
"""
n_train = x_train.shape[0]
dis = []
for i in range(n_train):
dis.append(np.dot(sample-x_train[i,:],sample-x_train[i,:].T))
minIndx = np.argmin(dis)
return y_train[minIndx]
测试代码
from sklearn import datasets
from Include.chapter3 import function
import numpy as np
#读取数据
digits = datasets.load_digits()
x , y = digits.data,digits.target
#划分数据集
x_train, y_train, x_test, y_test = function.train_test_split(x,y)
testId = np.random.randint(0, x_test.shape[0])
sample = x_test[testId, :]
#模板匹配
ans = function.neartemplet(x_train,y_train,sample)
y_test[testId]
print("预测的数字类型",ans)
print("真实的数字类型",y_test[testId])
测试结果
预测的数字类型 7
真实的数字类型 7