Iris数据集（鸢尾花数据集下载，密码：ae1e）是常用的分类实验数据集，由Fisher,1936收集整理。Iris也称鸢尾花卉数据集，是一类多重变量分析的数据集。数据集包含150个数据集，分为3类，每类50个数据，每个数据包含4个属性。可通过花萼长度，花萼宽度，花瓣长度，花瓣宽度4个属性预测鸢尾花卉属于（Setosa，Versicolour，Virginica）三个种类中的哪一类。
iris以鸢尾花的特征作为数据来源，常用在分类操作中。该数据集由3种不同类型的鸢尾花的50个样本数据构成。其中的一个种类与另外两个种类是线性可分离的，后两个种类是非线性可分离的。
该数据集包含了4个属性：
Sepal.Length（花萼长度），单位是cm;
Sepal.Width（花萼宽度），单位是cm;
Petal.Length（花瓣长度），单位是cm;
Petal.Width（花瓣宽度），单位是cm;
种类：Iris Setosa（1.山鸢尾）、Iris Versicolour（2.杂色鸢尾），以及Iris Virginica（3.维吉尼亚鸢尾）。
from __future__ import division
import math
import random
import pandas as pd
flowerLables = {0: 'Iris-setosa',
1: 'Iris-versicolor',
2: 'Iris-virginica'}
random.seed(0)
# 生成区间[a, b)内的随机数
def rand(a, b):
return (b - a) * random.random() + a
# 生成大小 I*J 的矩阵，默认零矩阵
def makeMatrix(I, J, fill=0.0):
m = []
for i in range(I):
m.append([fill] * J)
return m
# 函数 sigmoid
def sigmoid(x):
return 1.0 / (1.0 + math.exp(-x))
# 函数 sigmoid 的导数
def dsigmoid(x):
return x * (1 - x)
class NN:
""" 三层反向传播神经网络 """
def __init__(self, ni, nh, no):
# 输入层、隐藏层、输出层的节点（数）
self.ni = ni + 1 # 增加一个偏差节点
self.nh = nh + 1
self.no = no
# 激活神经网络的所有节点（向量）
self.ai = [1.0] * self.ni
self.ah = [1.0] * self.nh
self.ao = [1.0] * self.no
# 建立权重（矩阵）
self.wi = makeMatrix(self.ni, self.nh)
self.wo = makeMatrix(self.nh, self.no)
# 设为随机值
for i in range(self.ni):
for j in range(self.nh):
self.wi[i][j] = rand(-0.2, 0.2)
for j in range(self.nh):
for k in range(self.no):
self.wo[j][k] = rand(-2, 2)
def update(self, inputs):
if len(inputs) != self.ni - 1:
raise ValueError('与输入层节点数不符！')
# 激活输入层
for i in range(self.ni - 1):
self.ai[i] = inputs[i]
# 激活隐藏层
for j in range(self.nh):
sum = 0.0
for i in range(self.ni):
sum = sum + self.ai[i] * self.wi[i][j]
self.ah[j] = sigmoid(sum)
# 激活输出层
for k in range(self.no):
sum = 0.0
for j in range(self.nh):
sum = sum + self.ah[j] * self.wo[j][k]
self.ao[k] = sigmoid(sum)
return self.ao[:]
def backPropagate(self, targets, lr):
""" 反向传播 """
# 计算输出层的误差
output_deltas = [0.0] * self.no
for k in range(self.no):
error = targets[k] - self.ao[k]
output_deltas[k] = dsigmoid(self.ao[k]) * error
# 计算隐藏层的误差
hidden_deltas = [0.0] * self.nh
for j in range(self.nh):
error = 0.0
for k in range(self.no):
error = error + output_deltas[k] * self.wo[j][k]
hidden_deltas[j] = dsigmoid(self.ah[j]) * error
# 更新输出层权重
for j in range(self.nh):
for k in range(self.no):
change = output_deltas[k] * self.ah[j]
self.wo[j][k] = self.wo[j][k] + lr * change
# 更新输入层权重
for i in range(self.ni):
for j in range(self.nh):
change = hidden_deltas[j] * self.ai[i]
self.wi[i][j] = self.wi[i][j] + lr * change
# 计算误差
error = 0.0
error += 0.5 * (targets[k] - self.ao[k]) ** 2
return error
def test(self, patterns):
count = 0
for p in patterns:
target = flowerLables[(p[1].index(1))]
result = self.update(p[0])
index = result.index(max(result))
print(p[0], ':', target, '->', flowerLables[index])
count += (target == flowerLables[index])
accuracy = float(count / len(patterns))
print('accuracy: %-.9f' % accuracy)
def weights(self):
print('输入层权重:')
for i in range(self.ni):
print(self.wi[i])
print()
print('输出层权重:')
for j in range(self.nh):
print(self.wo[j])
def train(self, patterns, iterations=1000, lr=0.1):
# lr: 学习速率(learning rate)
for i in range(iterations):
error = 0.0
for p in patterns:
inputs = p[0]
targets = p[1]
self.update(inputs)
error = error + self.backPropagate(targets, lr)
if i % 100 == 0:
print('error: %-.9f' % error)
def iris():
data = []
# 读取数据
raw = pd.read_csv('iris.csv')
raw_data = raw.values
raw_feature = raw_data[0:, 0:4]
for i in range(len(raw_feature)):
ele = []
ele.append(list(raw_feature[i]))
if raw_data[i][4] == 'Iris-setosa':
ele.append([1, 0, 0])
elif raw_data[i][4] == 'Iris-versicolor':
ele.append([0, 1, 0])
else:
ele.append([0, 0, 1])
data.append(ele)
# 随机排列数据
random.shuffle(data)
training = data[0:100]
test = data[101:]
nn = NN(4, 7, 3)
nn.train(training, iterations=10000)
nn.test(test)
if __name__ == '__main__':
iris()训练结果：
error: 2.769657180
error: 0.007947966
error: 0.003326474
error: 0.002045720
error: 0.001470802
error: 0.001146268
error: 0.000938282
error: 0.000793788
error: 0.000687639
error: 0.000606396
error: 0.000542234
error: 0.000490292
error: 0.000447389
error: 0.000411360
error: 0.000380678
error: 0.000354238
error: 0.000331219
error: 0.000310998
error: 0.000293096
error: 0.000277135
error: 0.000262817
error: 0.000249901
error: 0.000238191
error: 0.000227525
error: 0.000217771
error: 0.000208817
error: 0.000200568
error: 0.000192943
error: 0.000185876
error: 0.000179306
error: 0.000173184
error: 0.000167465
error: 0.000162111
error: 0.000157087
error: 0.000152365
error: 0.000147917
error: 0.000143722
error: 0.000139757
error: 0.000136004
error: 0.000132447
error: 0.000129071
error: 0.000125863
error: 0.000122810
error: 0.000119901
error: 0.000117126
error: 0.000114476
error: 0.000111944
error: 0.000109521
error: 0.000107200
error: 0.000104976
error: 0.000102841
error: 0.000100792
error: 0.000098822
error: 0.000096928
error: 0.000095105
error: 0.000093349
error: 0.000091657
error: 0.000090024
error: 0.000088449
error: 0.000086928
error: 0.000085458
error: 0.000084037
error: 0.000082662
error: 0.000081332
error: 0.000080044
error: 0.000078795
error: 0.000077585
error: 0.000076412
error: 0.000075273
error: 0.000074168
error: 0.000073095
error: 0.000072052
error: 0.000071038
error: 0.000070053
error: 0.000069094
error: 0.000068162
error: 0.000067254
error: 0.000066370
error: 0.000065509
error: 0.000064670
error: 0.000063852
error: 0.000063054
error: 0.000062276
error: 0.000061517
error: 0.000060777
error: 0.000060053
error: 0.000059347
error: 0.000058658
error: 0.000057984
error: 0.000057325
error: 0.000056681
error: 0.000056052
error: 0.000055436
error: 0.000054833
error: 0.000054244
error: 0.000053667
error: 0.000053102
error: 0.000052549
error: 0.000052007
error: 0.000051477
[81, 5.5, 2.4, 3.8] : Iris-virginica -> Iris-virginica
[86, 6.0, 3.4, 4.5] : Iris-virginica -> Iris-virginica
[101, 6.3, 3.3, 6.0] : Iris-virginica -> Iris-virginica
[91, 5.5, 2.6, 4.4] : Iris-virginica -> Iris-virginica
[52, 6.4, 3.2, 4.5] : Iris-virginica -> Iris-virginica
[93, 5.8, 2.6, 4.0] : Iris-virginica -> Iris-virginica
[12, 4.8, 3.4, 1.6] : Iris-virginica -> Iris-virginica
[2, 4.9, 3.0, 1.4] : Iris-virginica -> Iris-virginica
[118, 7.7, 3.8, 6.7] : Iris-virginica -> Iris-virginica
[144, 6.8, 3.2, 5.9] : Iris-virginica -> Iris-virginica
[8, 5.0, 3.4, 1.5] : Iris-virginica -> Iris-virginica
[34, 5.5, 4.2, 1.4] : Iris-virginica -> Iris-virginica
[147, 6.3, 2.5, 5.0] : Iris-virginica -> Iris-virginica
[111, 6.5, 3.2, 5.1] : Iris-virginica -> Iris-virginica
[57, 6.3, 3.3, 4.7] : Iris-virginica -> Iris-virginica
[62, 5.9, 3.0, 4.2] : Iris-virginica -> Iris-virginica
[71, 5.9, 3.2, 4.8] : Iris-virginica -> Iris-virginica
[27, 5.0, 3.4, 1.6] : Iris-virginica -> Iris-virginica
[117, 6.5, 3.0, 5.5] : Iris-virginica -> Iris-virginica
[82, 5.5, 2.4, 3.7] : Iris-virginica -> Iris-virginica
[79, 6.0, 2.9, 4.5] : Iris-virginica -> Iris-virginica
[41, 5.0, 3.5, 1.3] : Iris-virginica -> Iris-virginica
[140, 6.9, 3.1, 5.4] : Iris-virginica -> Iris-virginica
[46, 4.8, 3.0, 1.4] : Iris-virginica -> Iris-virginica
[13, 4.8, 3.0, 1.4] : Iris-virginica -> Iris-virginica
[72, 6.1, 2.8, 4.0] : Iris-virginica -> Iris-virginica
[121, 6.9, 3.2, 5.7] : Iris-virginica -> Iris-virginica
[85, 5.4, 3.0, 4.5] : Iris-virginica -> Iris-virginica
[19, 5.7, 3.8, 1.7] : Iris-virginica -> Iris-virginica
[26, 5.0, 3.0, 1.6] : Iris-virginica -> Iris-virginica
[80, 5.7, 2.6, 3.5] : Iris-virginica -> Iris-virginica
[38, 4.9, 3.6, 1.4] : Iris-virginica -> Iris-virginica
[65, 5.6, 2.9, 3.6] : Iris-virginica -> Iris-virginica
[25, 4.8, 3.4, 1.9] : Iris-virginica -> Iris-virginica
[138, 6.4, 3.1, 5.5] : Iris-virginica -> Iris-virginica
[73, 6.3, 2.5, 4.9] : Iris-virginica -> Iris-virginica
[36, 5.0, 3.2, 1.2] : Iris-virginica -> Iris-virginica
[130, 7.2, 3.0, 5.8] : Iris-virginica -> Iris-virginica
[56, 5.7, 2.8, 4.5] : Iris-virginica -> Iris-virginica
[92, 6.1, 3.0, 4.6] : Iris-virginica -> Iris-virginica
[123, 7.7, 2.8, 6.7] : Iris-virginica -> Iris-virginica
[78, 6.7, 3.0, 5.0] : Iris-virginica -> Iris-virginica
[104, 6.3, 2.9, 5.6] : Iris-virginica -> Iris-virginica
[125, 6.7, 3.3, 5.7] : Iris-virginica -> Iris-virginica
[131, 7.4, 2.8, 6.1] : Iris-virginica -> Iris-virginica
[67, 5.6, 3.0, 4.5] : Iris-virginica -> Iris-virginica
[11, 5.4, 3.7, 1.5] : Iris-virginica -> Iris-virginica
[108, 7.3, 2.9, 6.3] : Iris-virginica -> Iris-virginica
[99, 5.1, 2.5, 3.0] : Iris-virginica -> Iris-virginica
accuracy: 1.000000000转载：https://blog.csdn.net/qq_42570457/article/details/81454512