标签：KNN csv train file test 手写 识别 array def
from numpy import*
import csv
import operator
from sklearn.neighbors import KNeighborsClassifier
def toInt(array):
array = mat(array)
m, n = shape(array)
Array=zeros((m,n))
for i in range(m):
for j in range(n):
try:
Array[i, j]=int(array[i, j])
except ValueError:
continue
return Array
def nomalizing(array):
m,n=shape(array)
for i in range(m):
for j in range(n):
if array[i, j] != 0:
array[i, j] = 1
return array
def loadTrainData():
l = []
with open("train.csv") as file:
lines = csv.reader(file)
for line in lines:
l.append(line)
file.close()
l.remove(l[0])
l = array(l)
label = l[:, 0]
data = l[:, 1:]
return nomalizing(toInt(data)),toInt(label)
def loadTestData():
l = []
with open("test.csv") as file:
lines = csv.reader(file)
for line in lines:
l.append(line)
file.close()
l.remove(l[0])
data = array(l)
return nomalizing(toInt(data))
def loadTest_result():
l = []
with open("test_result.csv") as file:
lines = csv.reader(file)
for line in lines:
l.append(line)
file.close()
l.remove(l[0])
label = array(l)
return toInt(label[:, 1])
def saveResult(result):
l = []
with open("my_result.csv","w")as myFile:
myWriter = csv.writer(myFile)
for i in result:
l.append(i)
myWriter.writerow(l)
myFile.close()
return;
def knnClassify(x_train, y_train, x_test):
estimator = KNeighborsClassifier()
estimator.fit(x_train, ravel(y_train))
y_test = estimator.predict(x_test)
saveResult(y_test)
return y_test
def digitRecognition():
x_train, y_train = loadTrainData()
x_test = loadTestData()
predict = knnClassify(x_train, y_train, x_test)
y_test = loadTest_result()
m, n = shape(x_test)
wrong = 0
for i in range(m):
# print("predict: %d, answer: %d" %(predict[i], y_test[0, i]))
if predict[i] != y_test[0, i]:
wrong += 1
print("wrong = %d" % wrong) #819
print("right rate = %f%%" % (100.0 * (m - wrong) / float(m))) #97.075%
if __name__ == "__main__":
digitRecognition()
九克拉
发布了3 篇原创文章 · 获赞 0 · 访问量 29
私信
关注
标签：KNN,csv,train,file,test,手写,识别,array,def 来源： https://blog.csdn.net/qq_45807398/article/details/104161642
本站声明： 1. iCode9 技术分享网（下文简称本站）提供的所有内容，仅供技术学习、探讨和分享； 2. 关于本站的所有留言、评论、转载及引用，纯属内容发起人的个人观点，与本站观点和立场无关； 3. 关于本站的所有言论和文字，纯属内容发起人的个人观点，与本站观点和立场无关； 4. 本站文章均是网友提供，不完全保证技术分享内容的完整性、准确性、时效性、风险性和版权归属；如您发现该文章侵犯了您的权益，可联系我们第一时间进行删除； 5. 本站为非盈利性的个人网站，所有内容不会用来进行牟利，也不会利用任何形式的广告来间接获益，纯粹是为了广大技术爱好者提供技术内容和技术思想的分享性交流网站。