水果部分數(shù)據(jù)
捕獲.PNG
代碼
import numpy as np
import math
import csv
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pylab as pl
import random
from matplotlib import cm
from sklearn.model_selection import train_test_split
# 求平均值
def mean(numbers):
return sum(numbers)/float(len(numbers))
# 求平均差
def stdev(numbers):
avg = mean(numbers)
variance = sum([pow(x-avg,2) for x in numbers])/float(len(numbers)-1)
return math.sqrt(variance)
# 求各列的平均值和方差--提取數(shù)據(jù)特征
def summarize(dataset):
parameter = [(mean(attribute), stdev(attribute)) for attribute in zip(*dataset)]
#parameter = [(mean(dataset.iloc[:,i]),stdev(dataset.iloc[:,i])) for i in range(dataset.shape[1]) ]
del parameter[-1]
return parameter
# 進行分類
def separatedByClass(dataset):
separated = {}
#創(chuàng)建字典
for i in range(len(dataset)):
vector = dataset[i]
if (vector[-1] not in separated):
#根據(jù)最后一個元素佛掖，隨后一個元素為1,2,3,4菜职，代表著水果的種類巍棱，作為鍵值key
separated[vector[-1]] = []
separated[vector[-1]].append(vector)
return separated
# 類別屬性提取特征,即每一類四種特征總的均值和方差
def summarizeByClass(dataset):
separated = separatedByClass(dataset)
summaries = { }
#創(chuàng)建字典
for classValue, instances in separated.items():
summaries[classValue] = summarize(instances)
return summaries
# 求出高斯概率密度函數(shù)
def calculateProbability(x, mean, stdev):
exponent = math.exp(-(math.pow(x - mean, 2) / (2 * math.pow(stdev, 2))))
return (1 / (math.sqrt(2 * math.pi) * stdev)) * exponent
#所屬類的概率
def calculateClassProbabilities(summaries, inputVector):
probabilities = {}
#字典
for classValue, classSummaries in summaries.items():
probabilities[classValue] = 1
for i in range(len(classSummaries)):
mean, stdev = classSummaries[i]
x = inputVector[i]
probabilities[classValue] *= calculateProbability(x, mean, stdev)
#求出總的高斯密度的乘積
return probabilities
# 對數(shù)據(jù)單一預測
# 每組測試數(shù)據(jù)最有可能的情況
def predict(summaries, inputVector):
probabilities = calculateClassProbabilities(summaries, inputVector)
bestLabel, bestProb = None, -1
for classValue, probability in probabilities.items():
if bestLabel is None or probability > bestProb:
bestProb = probability
bestLabel = classValue
return bestLabel
#進行多重預測
def getPredictions(summaries, testSet):
predictions = [] #來存儲結果
for i in range(len(testSet)):
result = predict(summaries, testSet[i])
predictions.append(result)
return predictions # 最終返回輸出結果
#輸出結果計算準確率
def getAccuracy(testSet, predictions):
correct = 0
print("結果：")
for x in range(len(testSet)):
print("預測的結果：", predictions[x], "----", testSet[x][-1], "：正確的結果")
if testSet[x][-1] == predictions[x]:
correct += 1
return (correct / float(len(testSet))) * 100.0
def main():
fruits = pd.read_table('E:/fruit.txt') #fruit.txt所在位置，我將它放在E盤澄耍。
feature_names = ['fruit_label', 'mass', 'width', 'height', 'color_score']
X = fruits[['mass', 'width', 'height', 'color_score', 'fruit_label']]
Y = fruits['fruit_label']
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.25, random_state=0) #通過pandas取出數(shù)據(jù)，再隨機生成X_train和X_test 訓練和測試數(shù)據(jù)
Traindataset = X_train.values
Testdataset = X_test.values
'''
代碼原因將數(shù)據(jù)轉換成一下格式，目的是為了去掉pandas中dataframe的index绸硕，如mass,width 等特征值
mass width height color_score fruit_label
42 154 7.2 7.2 0.82 3
48 174 7.3 10.1 0.72 4
變成
[[154. 7.2 7.2 0.82 3. ]
[174. 7.3 10.1 0.72 4. ]
[ 76. 5.8 4. 0.81 2. ]]
'''
summaries = summarizeByClass(Traindataset) #根據(jù)測試數(shù)據(jù)進行提取數(shù)據(jù)特征羞芍， 分類哗戈，求方差，均值荷科，然后對每類進行特征值提取
print("特征的提任ㄒА：",summaries) #輸出貝葉斯整理的結果
predictions = getPredictions(summaries, Testdataset) #輸入測試數(shù)據(jù)
accuracy = getAccuracy(Testdataset, predictions)
print("準確率：",accuracy,'%')
if __name__ == "__main__":
main()
運行結果
捕獲1.PNG
