文章目录
- 简单分类模型 - 逻辑回归
-
- 1.1 准备数据
- 1.2 定义假设函数
- 1.3 定义代价函数
- 1.4 定义梯度下降算法
- 1.5 绘制决策边界
- 1.6 计算准确率
- 1.7 试试用Sklearn来解决
- 2.1 准备数据(试试第二个例子)
- 2.2 假设函数与前h相同
- 2.3 代价函数与前相同
- 2.4 梯度下降算法与前相同
- 2.5 欠拟合的了（模型过于简单，增加一些多项式特征）
- 2.6 定义正则化项的代价函数
- 2.7 定义正则化的梯度下降算法
- 实验1 计算基于正则化得到的准确率
- 2.8 试试sklearn
- 参考
- 实验2 完成3.2 调用逻辑回归模型完成分类
- 实验3 完成3.3 调用正则化的逻辑回归模型完成分类
- 实验4 完成3.3 调用SKLEARN完成分类
简单分类模型 - 逻辑回归
在这一次练习中，我们将要实现逻辑回归并且应用到一个分类任务。我们还将通过将正则化加入训练算法，来提高算法的鲁棒性，并用更复杂的情形来测试它。
1.1 准备数据
本实验的数据包含两个变量(评分1和评分2，可以看作是特征),某大学的管理者，想通过申请学生两次测试的评分，来决定他们是否被录取。因此，构建一个可以基于两次测试评分来评估录取可能性的分类模型。
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
#利用pandas显示数据
path = 'ex2data1.txt'
data = pd.read_csv(path, header=None, names=['Exam1', 'Exam2', 'Admitted'])
data.head()
| Exam1 | Exam2 | Admitted | |
|---|---|---|---|
| 0 | 34.623660 | 78.024693 | 0 |
| 1 | 30.286711 | 43.894998 | 0 |
| 2 | 35.847409 | 72.902198 | 0 |
| 3 | 60.182599 | 86.308552 | 1 |
| 4 | 79.032736 | 75.344376 | 1 |
data.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 100 entries, 0 to 99
Data columns (total 3 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Exam1 100 non-null float64
1 Exam2 100 non-null float64
2 Admitted 100 non-null int64
dtypes: float64(2), int64(1)
memory usage: 2.5 KB
#看看数据的形状
data.shape
(100, 3)
让我们创建两个分数的散点图，并使用颜色编码来可视化，如果样本是正的（被接纳）或负的（未被接纳）。
positive_index=data["Admitted"].isin([1])
negative_index=data["Admitted"].isin([0])
positive_index
0 False
1 False
2 False
3 True
4 True
...
95 True
96 True
97 True
98 True
99 True
Name: Admitted, Length: 100, dtype: bool
plt.scatter(data[positive_index]["Exam1"],data[positive_index]["Exam2"],color="red",marker="+")
plt.scatter(data[negative_index]["Exam1"],data[negative_index]["Exam2"],color="blue",marker="o")
plt.legend(["admitted","Not admitted"])
plt.xlabel("Exam1")
plt.ylabel("Exam2")
plt.show()
positive = data[data['Admitted'].isin([1])]
negative = data[data['Admitted'].isin([0])]
fig, ax = plt.subplots(figsize=(6,4))
ax.scatter(positive['Exam1'],
positive['Exam2'],
s=50,
c='b',
marker='o',
label='Admitted')
ax.scatter(negative['Exam1'],
negative['Exam2'],
s=50,
c='r',
marker='x',
label='Not Admitted')
ax.legend()
ax.set_xlabel('Exam 1 Score')
ax.set_ylabel('Exam 2 Score')
plt.show()
看起来在两类间，有一个清晰的决策边界。现在我们需要实现逻辑回归，那样就可以训练一个模型来预测结果。
#准备训练数据
col_num=data.shape[1]
X=data.iloc[:,:col_num-1]
y=data.iloc[:,col_num-1]
X.insert(0,"ones",1)
X.shape
(100, 3)
X=X.values
X.shape
(100, 3)
y=y.values
y.shape
(100,)
1.2 定义假设函数
Sigmoid 函数
g g g 代表一个常用的逻辑函数（logistic function）为 S S S形函数（Sigmoid function），公式为：
g ( z ) = 1 1 + e − z g(z)=\frac{1}{1+{
{e}^{-z}}} g(z)=1+e−z1
合起来，我们得到逻辑回归模型的假设函数：
h ( x ) = 1 1 + e − w T x {
{h}}\left( x \right)=\frac{1}{1+{
{e}^{-{
{w }^{T}}x}}} h(x)=1+e−wTx1
def sigmoid(z):
return 1 / (1 + np.exp(-z))
让我们做一个快速的检查，来确保它可以工作。
nums = np.arange(-10, 10, step=1)
fig, ax = plt.subplots(figsize=(6, 4))
ax.plot(nums, sigmoid(nums), 'r')
plt.show()
w=np.zeros((X.shape[1],1))
#定义假设函数h(x)=1/(1+exp^(-w.Tx))
def h(X,w):
z=X@w
h=sigmoid(z)
return h
1.3 定义代价函数
y_hat=sigmoid(X@w)
X.shape,y.shape,np.log(y_hat).shape
((100, 3), (100,), (100, 1))
现在，我们需要编写代价函数来评估结果。
代价函数：
J ( w ) = − 1 m ∑ i = 1 m ( y ( i ) log ( h ( x ( i ) ) ) + ( 1 − y ( i ) ) log ( 1 − h ( x ( i ) ) ) ) J\left(w\right)=-\frac{1}{m}\sum\limits_{i=1}^{m}{({
{y}^{(i)}}\log \left( {h}\left( {
{x}^{(i)}} \right) \right)+\left( 1-{
{y}^{(i)}} \right)\log \left( 1-{h}\left( {
{x}^{(i)}} \right) \right))} J(w)=−m1i=1∑m(y(i)log(h(x(i)))+(1−y(i))log(1−h(x(i))))
#代价函数构造
def cost(X,w,y):
#当X(m,n+1),y(m,),w(n+1,1)
y_hat=sigmoid(X@w)
right=np.multiply(y.ravel(),np.log(y_hat).ravel())+np.multiply((1-y).ravel(),np.log(1-y_hat).ravel())
cost=-np.sum(right)/X.shape[0]
return cost
#设置初始的权值
w=np.zeros((X.shape[1],1))
#查看初始的代价
cost(X,w,y)
0.6931471805599453
看起来不错，接下来，我们需要一个函数来计算我们的训练数据、标签和一些参数 w w w的梯度。
1.4 定义梯度下降算法
gradient descent(梯度下降)
- 这是批量梯度下降（batch gradient descent）
- 转化为向量化计算： 1 m X T ( S i g m o i d ( X W ) − y ) \frac{1}{m} X^T( Sigmoid(XW) - y ) m1XT(Sigmoid(XW)−y)
∂ J ( w ) ∂ w j = 1 m ∑ i = 1 m ( h ( x ( i ) ) − y ( i ) ) x j ( i ) \frac{\partial J\left( w \right)}{\partial { {w }_{j}}}=\frac{1}{m}\sum\limits_{i=1}^{m}{({ {h}}\left( { {x}^{(i)}} \right)-{ {y}^{(i)}})x_{_{j}}^{(i)}} ∂wj∂J(w)=m1i=1∑m(h(x(i))−y(i))xj(i)
def grandient(X,y,iter_num,alpha):
y=y.reshape((X.shape[0],1))
w=np.zeros((X.shape[1],1))
cost_lst=[]
for i in range(iter_num):
y_pred=h(X,w)-y
temp=np.zeros((X.shape[1],1))
for j in range(X.shape[1]):
right=np.multiply(y_pred.ravel(),X[:,j])
gradient=1/(X.shape[0])*(np.sum(right))
temp[j,0]=w[j,0]-alpha*gradient
w=temp
cost_lst.append(cost(X,w,y.ravel()))
return w,cost_lst
iter_num,alpha=1000000,0.001
w,cost_lst=grandient(X,y,iter_num,alpha)
cost_lst[iter_num-1]
0.22465416189188264
plt.plot(range(iter_num),cost_lst,"b-o")
[<matplotlib.lines.Line2D at 0x14224c08190>]
Xw—X(m,n) w (n,1)
w
array([[-15.39517866],
[ 0.12825989],
[ 0.12247929]])
1.5 绘制决策边界
0=w[0,0]+w[1,0]*x1+w[2,0]*x2,令y=0 可以得到x2和x1的关系为
x2=(-w[0,0]-w[1,0]*x1)/w[2,0]
#绘图
x_exma1=np.linspace(data["Exam1"].min(),data["Exam1"].max(),100)
x2=(-w[0,0]-w[1,0]*x_exma1)/(w[2,0])
plt.plot(x_exma1,x2,"r-")
plt.scatter(data[positive_index]["Exam1"],data[positive_index]["Exam2"],color="c",marker="^")
plt.scatter(data[negative_index]["Exam1"],data[negative_index]["Exam2"],color="b",marker="o")
plt.show()
1.6 计算准确率
如何用我们所学的参数w来为数据集X输出预测，来给我们的分类器的训练精度打分。
逻辑回归模型的假设函数：
h ( x ) = 1 1 + e − w T X {
{h}}\left( x \right)=\frac{1}{1+{
{e}^{-{
{w }^{T}}X}}} h(x)=1+e−wTX1
当 h { {h}} h大于等于0.5时，预测 y=1
当 h { {h}} h小于0.5时，预测 y=0 。
y_p_true=(h(X,w)>0.5).ravel()
y_p_true
array([False, False, False, True, True, False, True, False, True,
True, True, False, True, True, False, True, False, False,
True, True, False, True, False, False, True, True, True,
True, False, False, True, True, False, False, False, False,
True, True, False, False, True, False, True, True, False,
False, True, True, True, True, True, True, True, False,
False, False, True, True, True, True, True, False, False,
False, False, False, True, False, True, True, False, True,
True, True, True, True, True, True, False, True, True,
True, True, False, True, True, False, True, True, False,
True, True, False, True, True, True, True, True, False,
True])
y_p_pred=(data["Admitted"]==1).values
y_p_pred
array([False, False, False, True, True, False, True, True, True,
True, False, False, True, True, False, True, True, False,
True, True, False, True, False, False, True, True, True,
False, False, False, True, True, False, True, False, False,
False, True, False, False, True, False, True, False, False,
False, True, True, True, True, True, True, True, False,
False, False, True, False, True, True, True, False, False,
False, False, False, True, False, True, True, False, True,
True, True, True, True, True, True, False, False, True,
True, True, True, True, True, False, True, True, False,
True, True, False, True, True, True, True, True, True,
True])
np.sum(y_p_pred==y_p_true)/X.shape[0]
0.89
1.7 试试用Sklearn来解决
from sklearn.linear_model import LogisticRegression
clf = LogisticRegression().fit(X, y)
clf.score(X,y)
0.89
clf.predict(X)
array([0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1,
0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1,
0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0,
1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1,
1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1], dtype=int64)
np.array([1 if item>0.5 else 0 for item in h(X,w)])
array([0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1,
0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1,
0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0,
1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1,
1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1])
np.argmax(clf.predict_proba(X),axis=1)
array([0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1,
0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1,
0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0,
1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1,
1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1], dtype=int64)
X.shape,y.shape
((100, 3), (100,))
from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression
y
clf = LogisticRegression().fit(X, y)
clf.predict(X)
array([0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1,
0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1,
0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0,
1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1,
1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1], dtype=int64)
clf.predict(X).shape
(100,)
y.shape
(100,)
np.sum(clf.predict(X)==y.ravel())/np.sum(X.shape[0])
0.89
#所以分类问题中的score用的是准确率
clf.score(X,y)
0.89
我们的逻辑回归分类器预测正确，如果一个学生被录取或没有录取，达到89%的精确度。不坏！记住，这是训练集的准确性。我们没有保持住了设置或使用交叉验证得到的真实逼近，所以这个数字有可能高于其真实值（这个话题将在以后说明）。
2.1 准备数据(试试第二个例子)
在训练的第二部分，我们将要通过加入正则项提升逻辑回归算。简而言之，正则化是成本函数中的一个术语，它使算法更倾向于“更简单”的模型（在这种情况下，模型将更小的系数）。这个理论助于减少过拟合，提高模型的泛化能力。
设想你是工厂的生产主管，你有一些芯片在两次测试中的测试结果。对于这两次测试，你想决定是否芯片要被接受或抛弃。为了帮助你做出艰难的决定，你拥有过去芯片的测试数据集，从其中你可以构建一个逻辑回归模型。
和第一部分很像，从数据可视化开始吧！
#读取文件'ex2data2.txt'的数据
path="ex2data2.txt"
data2=pd.read_csv(path,header=None,names=["Test1","Test2","Accepted"])
data2.head()
| Test1 | Test2 | Accepted | |
|---|---|---|---|
| 0 | 0.051267 | 0.69956 | 1 |
| 1 | -0.092742 | 0.68494 | 1 |
| 2 | -0.213710 | 0.69225 | 1 |
| 3 | -0.375000 | 0.50219 | 1 |
| 4 | -0.513250 | 0.46564 | 1 |
#可视化数据
positive_index=data2["Accepted"]==1
negative_index=data2["Accepted"]==0
plt.scatter(data2[positive_index]["Test1"],data2[positive_index]["Test2"],color="r",marker="^")
plt.scatter(data2[negative_index]["Test1"],data2[negative_index]["Test2"],color="b",marker="o")
plt.legend(["Accpted","Not accepted"])
plt.show()
X2=data2.iloc[:,:2]
y2=data2.iloc[:,2]
X2.insert(0,"ones",1)
X2.shape,y2.shape
((118, 3), (118,))
X2=X2.values
y2=y2.values
2.2 假设函数与前h相同
2.3 代价函数与前相同
2.4 梯度下降算法与前相同
iter_num,alpha=600000,0.0005
w,cost_lst=grandient(X2,y2,iter_num,alpha)
#绘制误差曲线
plt.plot(range(iter_num),cost_lst,"b-o")
[<matplotlib.lines.Line2D at 0x1422d45e970>]
#看看准确率有多少
y_pred=[1 if item>=0.5 else 0 for item in sigmoid(X2@w).ravel()]
y_pred=np.array(y_pred)
y_pred.shape
(118,)
y2.shape
(118,)
np.sum(y_pred==y2)
65
np.sum(y_pred==y2)/y2.shape[0]
0.5508474576271186
y_pred=[1 if item>=0.5 else 0 for item in sigmoid(X2@w).ravel()]
y_pred=np.array(y_pred)
np.sum(y_pred==y2)/y2.shape[0]
0.5508474576271186
2.5 欠拟合的了（模型过于简单，增加一些多项式特征）
path="ex2data2.txt"
data2=pd.read_csv(path,header=None,names=["Test1","Test2","Accepted"])
data2.head()
| Test1 | Test2 | Accepted | |
|---|---|---|---|
| 0 | 0.051267 | 0.69956 | 1 |
| 1 | -0.092742 | 0.68494 | 1 |
| 2 | -0.213710 | 0.69225 | 1 |
| 3 | -0.375000 | 0.50219 | 1 |
| 4 | -0.513250 | 0.46564 | 1 |
#为数据框增加多列多项式特征
def poly_feature(data2,degree):
x1=data2["Test1"]
x2=data2["Test2"]
items=[]
for i in range(degree+1):
for j in range(degree-i+1):
data2["F"+str(i)+str(j)]=np.power(x1,i)*np.power(x2,j)
items.append("(x1**{})*(x2**{})".format(i,j))
data2=data2.drop(["Test1","Test2"],axis=1)
return data2,items
data2,items=poly_feature(data2,4)
data2.shape
(118, 16)
data2.head(5)
| Accepted | F00 | F01 | F02 | F03 | F04 | F10 | F11 | F12 | F13 | F20 | F21 | F22 | F30 | F31 | F40 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 1.0 | 0.69956 | 0.489384 | 0.342354 | 0.239497 | 0.051267 | 0.035864 | 0.025089 | 0.017551 | 0.002628 | 0.001839 | 0.001286 | 0.000135 | 0.000094 | 0.000007 |
| 1 | 1 | 1.0 | 0.68494 | 0.469143 | 0.321335 | 0.220095 | -0.092742 | -0.063523 | -0.043509 | -0.029801 | 0.008601 | 0.005891 | 0.004035 | -0.000798 | -0.000546 | 0.000074 |
| 2 | 1 | 1.0 | 0.69225 | 0.479210 | 0.331733 | 0.229642 | -0.213710 | -0.147941 | -0.102412 | -0.070895 | 0.045672 | 0.031616 | 0.021886 | -0.009761 | -0.006757 | 0.002086 |
| 3 | 1 | 1.0 | 0.50219 | 0.252195 | 0.126650 | 0.063602 | -0.375000 | -0.188321 | -0.094573 | -0.047494 | 0.140625 | 0.070620 | 0.035465 | -0.052734 | -0.026483 | 0.019775 |
| 4 | 1 | 1.0 | 0.46564 | 0.216821 | 0.100960 | 0.047011 | -0.513250 | -0.238990 | -0.111283 | -0.051818 | 0.263426 | 0.122661 | 0.057116 | -0.135203 | -0.062956 | 0.069393 |
X2=data2.iloc[:,1:data2.shape[1]-1]
y2=data2.iloc[:,0]
X2.shape,y.shape
((118, 14), (100,))
X2
| F00 | F01 | F02 | F03 | F04 | F10 | F11 | F12 | F13 | F20 | F21 | F22 | F30 | F31 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1.0 | 0.699560 | 0.489384 | 0.342354 | 2.394969e-01 | 0.051267 | 0.035864 | 0.025089 | 0.017551 | 0.002628 | 0.001839 | 0.001286 | 1.347453e-04 | 9.426244e-05 |
| 1 | 1.0 | 0.684940 | 0.469143 | 0.321335 | 2.200950e-01 | -0.092742 | -0.063523 | -0.043509 | -0.029801 | 0.008601 | 0.005891 | 0.004035 | -7.976812e-04 | -5.463638e-04 |
| 2 | 1.0 | 0.692250 | 0.479210 | 0.331733 | 2.296423e-01 | -0.213710 | -0.147941 | -0.102412 | -0.070895 | 0.045672 | 0.031616 | 0.021886 | -9.760555e-03 | -6.756745e-03 |
| 3 | 1.0 | 0.502190 | 0.252195 | 0.126650 | 6.360222e-02 | -0.375000 | -0.188321 | -0.094573 | -0.047494 | 0.140625 | 0.070620 | 0.035465 | -5.273438e-02 | -2.648268e-02 |
| 4 | 1.0 | 0.465640 | 0.216821 | 0.100960 | 4.701118e-02 | -0.513250 | -0.238990 | -0.111283 | -0.051818 | 0.263426 | 0.122661 | 0.057116 | -1.352032e-01 | -6.295600e-02 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 113 | 1.0 | 0.538740 | 0.290241 | 0.156364 | 8.423971e-02 | -0.720620 | -0.388227 | -0.209153 | -0.112679 | 0.519293 | 0.279764 | 0.150720 | -3.742131e-01 | -2.016035e-01 |
| 114 | 1.0 | 0.494880 | 0.244906 | 0.121199 | 5.997905e-02 | -0.593890 | -0.293904 | -0.145447 | -0.071979 | 0.352705 | 0.174547 | 0.086380 | -2.094682e-01 | -1.036616e-01 |
| 115 | 1.0 | 0.999270 | 0.998541 | 0.997812 | 9.970832e-01 | -0.484450 | -0.484096 | -0.483743 | -0.483390 | 0.234692 | 0.234520 | 0.234349 | -1.136964e-01 | -1.136134e-01 |
| 116 | 1.0 | 0.999270 | 0.998541 | 0.997812 | 9.970832e-01 | -0.006336 | -0.006332 | -0.006327 | -0.006323 | 0.000040 | 0.000040 | 0.000040 | -2.544062e-07 | -2.542205e-07 |
| 117 | 1.0 | -0.030612 | 0.000937 | -0.000029 | 8.781462e-07 | 0.632650 | -0.019367 | 0.000593 | -0.000018 | 0.400246 | -0.012252 | 0.000375 | 2.532156e-01 | -7.751437e-03 |
118 rows × 14 columns
y2
0 1
1 1
2 1
3 1
4 1
..
113 0
114 0
115 0
116 0
117 0
Name: Accepted, Length: 118, dtype: int64
X2=X2.values
y2=y2.values
X2.shape,y2.shape
((118, 14), (118,))
#虽然加了多项式特征，但是其他地方不需要改变
iter_num,alpha=600000,0.001
w,cost_lst=grandient(X2,y2,iter_num,alpha)
w,cost_lst
(array([[ 3.03503577],
[ 3.20158942],
[-4.0495866 ],
[-1.04983379],
[-3.95636068],
[ 2.0490215 ],
[-3.40302089],
[-0.79821365],
[-1.23393575],
[-7.32541507],
[-1.41115593],
[-1.80717912],
[-0.54355034],
[ 0.11775491]]),
[0.6931399371004173,
0.6931326952275558,
0.6931254549404754,
0.6931182162382921,
0.693110979120122,
0.6931037435850823,
0.69309650963229,
0.6930892772608634,
0.6930820464699207,
0.6930748172585808,
0.6930675896259637,
0.6930603635711893,
0.6930531390933783,
0.693045916191652,
0.6930386948651323,
0.6930314751129414,
0.6930242569342021,
0.6930170403280382,
0.6930098252935735,
0.6930026118299326,
0.6929953999362406,
0.6929881896116231,
0.6929809808552065,
0.6929737736661173,
0.6929665680434831,
0.6929593639864312,
0.6929521614940908,
0.6929449605655903,
0.6929377612000593,
0.6929305633966278,
0.6929233671544265,
0.6929161724725863,
0.6929089793502394,
0.6929017877865175,
0.6928945977805535,
0.6928874093314809,
0.6928802224384333,
0.6928730371005455,
0.6928658533169517,
0.6928586710867882,
0.6928514904091909,
0.6928443112832959,
0.6928371337082408,
0.6928299576831629,
0.6928227832072007,
0.6928156102794925,
0.692808438899178,
0.6928012690653973,
0.6927941007772903,
0.6927869340339977,
0.6927797688346614,
0.6927726051784232,
0.6927654430644256,
0.6927582824918116,
0.6927511234597252,
0.6927439659673101,
0.6927368100137112,
0.6927296555980735,
0.6927225027195429,
0.6927153513772657,
0.6927082015703889,
0.6927010532980594,
0.6926939065594254,
0.6926867613536353,
0.6926796176798382,
0.6926724755371833,
0.6926653349248207,
0.692658195841901,
0.6926510582875753,
0.6926439222609956,
0.6926367877613135,
0.6926296547876825,
0.6926225233392544,
0.6926153934151844,
0.6926082650146258,
0.6926011381367343,
0.6925940127806647,
0.6925868889455729,
0.6925797666306154,
0.6925726458349493,
0.692565526557732,
0.6925584087981212,
0.6925512925552758,
0.6925441778283548,
0.6925370646165178,
0.6925299529189249,
0.6925228427347367,
0.6925157340631142,
0.6925086269032195,
0.6925015212542147,
0.6924944171152624,
0.6924873144855257,
0.692480213364169,
0.6924731137503559,
0.692466015643252,
0.6924589190420221,
0.6924518239458325,
0.6924447303538497,
0.6924376382652399,
0.6924305476791713,
0.6924234585948119,
0.6924163710113299,
0.6924092849278943,
0.6924022003436747,
0.6923951172578418,
0.6923880356695654,
0.692380955578017,
0.6923738769823684,
0.6923667998817915,
0.6923597242754587,
0.6923526501625441,
0.6923455775422206,
0.6923385064136628,
0.6923314367760456,
0.6923243686285434,
0.6923173019703337,
0.6923102368005914,
0.6923031731184935,
0.6922961109232177,
0.6922890502139424,
0.6922819909898448,
0.6922749332501046,
0.692267876993901,
0.6922608222204141,
0.692253768928824,
0.6922467171183119,
0.6922396667880594,
0.6922326179372482,
0.692225570565061,
0.6922185246706808,
0.6922114802532913,
0.6922044373120759,
0.69219739584622,
0.692190355854908,
0.692183317337326,
0.6921762802926597,
0.6921692447200962,
0.6921622106188222,
0.6921551779880252,
0.6921481468268937,
0.6921411171346162,
0.6921340889103819,
0.6921270621533807,
0.6921200368628022,
0.6921130130378376,
0.6921059906776778,
0.6920989697815146,
0.6920919503485404,
0.6920849323779475,
0.6920779158689296,
0.6920709008206801,
0.6920638872323933,
0.6920568751032641,
0.6920498644324875,
0.6920428552192593,
0.692035847462776,
0.6920288411622341,
0.6920218363168312,
0.6920148329257646,
0.6920078309882332,
0.692000830503435,
0.6919938314705699,
0.6919868338888373,
0.6919798377574379,
0.6919728430755722,
0.6919658498424414,
0.6919588580572479,
0.6919518677191929,
0.6919448788274806,
0.6919378913813131,
0.6919309053798947,
0.6919239208224298,
0.691916937708123,
0.6919099560361796,
0.6919029758058055,
0.691895997016207,
0.6918890196665908,
0.6918820437561641,
0.6918750692841349,
0.6918680962497116,
0.6918611246521027,
0.6918541544905173,
0.691847185764166,
0.6918402184722583,
0.6918332526140052,
0.6918262881886181,
0.6918193251953085,
0.691812363633289,
0.691805403501772,
0.691798444799971,
0.6917914875270996,
0.6917845316823721,
0.6917775772650034,
0.6917706242742084,
0.6917636727092031,
0.6917567225692034,
0.6917497738534262,
0.6917428265610885,
0.6917358806914085,
0.691728936243604,
0.6917219932168934,
0.6917150516104965,
0.6917081114236324,
0.6917011726555214,
0.6916942353053842,
0.691687299372442,
0.6916803648559162,
0.691673431755029,
0.6916665000690029,
0.691659569797061,
0.6916526409384272,
0.691645713492325,
0.6916387874579791,
0.6916318628346149,
0.6916249396214572,
0.6916180178177327,
0.6916110974226677,
0.6916041784354889,
0.6915972608554238,
0.6915903446817003,
0.6915834299135475,
0.6915765165501937,
0.691569604590868,
0.6915626940348011,
0.6915557848812225,
0.6915488771293637,
0.6915419707784556,
0.6915350658277303,
0.69152816227642,
0.691521260123757,
0.6915143593689757,
0.6915074600113087,
0.6915005620499908,
0.6914936654842565,
0.6914867703133412,
0.6914798765364804,
0.6914729841529099,
0.691466093161867,
0.6914592035625885,
0.6914523153543117,
0.6914454285362747,
0.6914385431077166,
0.6914316590678757,
0.6914247764159921,
0.6914178951513054,
0.6914110152730563,
0.6914041367804855,
0.6913972596728342,
0.6913903839493448,
0.6913835096092594,
0.6913766366518207,
0.6913697650762726,
0.6913628948818578,
0.6913560260678212,
0.6913491586334077,
0.6913422925778623,
0.6913354279004306,
0.6913285646003587,
0.6913217026768933,
0.6913148421292818,
0.6913079829567716,
0.6913011251586102,
0.691294268734047,
0.6912874136823305,
0.6912805600027101,
0.6912737076944359,
0.6912668567567581,
0.6912600071889275,
0.691253158990196,
0.6912463121598151,
0.691239466697037,
0.6912326226011144,
0.6912257798713006,
0.6912189385068493,
0.6912120985070148,
0.6912052598710514,
0.6911984225982147,
0.6911915866877595,
0.6911847521389427,
0.69117791895102,
0.6911710871232487,
0.6911642566548867,
0.6911574275451913,
0.6911505997934211,
0.6911437733988343,
0.6911369483606911,
0.6911301246782512,
0.6911233023507742,
0.6911164813775215,
0.6911096617577531,
0.691102843490732,
0.6910960265757193,
0.6910892110119781,
0.6910823967987713,
0.6910755839353621,
0.6910687724210147,
0.6910619622549933,
0.6910551534365628,
0.6910483459649887,
0.6910415398395364,
0.6910347350594729,
0.6910279316240643,
0.6910211295325772,
0.6910143287842805,
0.6910075293784415,
0.6910007313143287,
0.6909939345912114,
0.6909871392083592,
0.6909803451650415,
0.6909735524605287,
0.6909667610940924,
0.6909599710650032,
0.6909531823725328,
0.6909463950159536,
0.6909396089945385,
0.6909328243075601,
0.6909260409542926,
0.6909192589340094,
0.6909124782459855,
0.6909056988894955,
0.690898920863815,
0.6908921441682196,
0.6908853688019859,
0.6908785947643907,
0.6908718220547111,
0.6908650506722244,
0.6908582806162092,
0.6908515118859441,
0.6908447444807079,
0.6908379783997801,
0.6908312136424405,
0.6908244502079699,
0.6908176880956487,
0.6908109273047588,
0.6908041678345811,
0.6907974096843985,
0.6907906528534933,
0.6907838973411485,
0.690777143146648,
0.6907703902692756,
0.6907636387083155,
0.6907568884630527,
0.690750139532773,
0.690743391916762,
0.6907366456143056,
0.6907299006246905,
0.6907231569472044,
0.6907164145811341,
0.6907096735257682,
0.690702933780395,
0.6906961953443035,
0.6906894582167828,
0.690682722397123,
0.6906759878846143,
0.6906692546785472,
0.6906625227782132,
0.6906557921829036,
0.6906490628919102,
0.6906423349045264,
0.6906356082200443,
0.6906288828377575,
0.6906221587569599,
0.6906154359769455,
0.6906087144970098,
0.6906019943164466,
0.6905952754345525,
0.6905885578506235,
0.6905818415639554,
0.6905751265738461,
0.690568412879592,
0.6905617004804914,
0.6905549893758427,
0.6905482795649438,
0.6905415710470946,
0.6905348638215943,
0.6905281578877431,
0.6905214532448414,
0.6905147498921895,
0.6905080478290895,
0.690501347054843,
0.6904946475687516,
0.6904879493701188,
0.6904812524582472,
0.6904745568324402,
0.6904678624920019,
0.6904611694362365,
0.6904544776644493,
0.6904477871759448,
0.6904410979700292,
0.6904344100460088,
0.6904277234031898,
0.6904210380408787,
0.6904143539583844,
0.6904076711550134,
0.6904009896300746,
0.6903943093828763,
0.6903876304127281,
0.6903809527189395,
0.6903742763008206,
0.6903676011576815,
0.6903609272888331,
0.6903542546935871,
0.6903475833712557,
0.69034091332115,
0.6903342445425832,
0.6903275770348686,
0.6903209107973187,
0.6903142458292487,
0.6903075821299722,
0.6903009196988041,
0.6902942585350598,
0.6902875986380544,
0.6902809400071047,
0.6902742826415266,
0.6902676265406373,
0.6902609717037543,
0.6902543181301951,
0.6902476658192781,
0.6902410147703216,
0.6902343649826451,
0.6902277164555679,
0.6902210691884098,
0.6902144231804913,
0.6902077784311333,
0.690201134939657,
0.6901944927053835,
0.6901878517276353,
0.6901812120057349,
0.6901745735390051,
0.6901679363267692,
0.6901613003683511,
0.6901546656630752,
0.6901480322102651,
0.6901414000092467,
0.6901347690593452,
0.6901281393598866,
0.690121510910197,
0.6901148837096033,
0.6901082577574326,
0.6901016330530122,
0.6900950095956704,
0.6900883873847353,
0.6900817664195361,
0.6900751466994021,
0.6900685282236624,
0.6900619109916476,
0.6900552950026879,
0.6900486802561143,
0.6900420667512582,
0.6900354544874515,
0.6900288434640263,
0.690022233680315,
0.6900156251356507,
0.6900090178293673,
0.6900024117607981,
0.6899958069292774,
0.6899892033341402,
0.6899826009747214,
0.6899759998503566,
0.6899693999603816,
0.6899628013041332,
0.6899562038809481,
0.6899496076901628,
0.6899430127311155,
0.6899364190031444,
0.6899298265055872,
0.6899232352377836,
0.6899166451990724,
0.6899100563887932,
0.6899034688062867,
0.6898968824508926,
0.6898902973219526,
0.6898837134188075,
0.6898771307407991,
0.6898705492872698,
0.6898639690575618,
0.6898573900510188,
0.6898508122669835,
0.6898442357048,
0.6898376603638127,
0.689831086243366,
0.6898245133428047,
0.6898179416614751,
0.689811371198722,
0.6898048019538924,
0.689798233926333,
0.6897916671153906,
0.6897851015204128,
0.6897785371407474,
0.6897719739757432,
0.6897654120247485,
0.6897588512871126,
0.6897522917621844,
0.6897457334493153,
0.6897391763478544,
0.689732620457153,
0.6897260657765623,
0.6897195123054336,
0.6897129600431194,
0.6897064089889716,
0.6896998591423432,
0.6896933105025878,
0.6896867630690583,
0.6896802168411088,
0.6896736718180946,
0.6896671279993697,
0.6896605853842896,
0.6896540439722101,
0.6896475037624867,
0.6896409647544766,
0.6896344269475361,
0.6896278903410226,
0.689621354934294,
0.6896148207267083,
0.6896082877176235,
0.6896017559063989,
0.6895952252923941,
0.689588695874968,
0.6895821676534813,
0.6895756406272939,
0.6895691147957672,
0.6895625901582623,
0.6895560667141409,
0.6895495444627653,
0.6895430234034974,
0.6895365035357004,
0.6895299848587378,
0.6895234673719729,
0.6895169510747703,
0.6895104359664939,
0.6895039220465085,
0.6894974093141799,
0.6894908977688735,
0.6894843874099555,
0.6894778782367924,
0.6894713702487508,
0.6894648634451982,
0.6894583578255021,
0.6894518533890304,
0.6894453501351522,
0.6894388480632355,
0.6894323471726499,
0.6894258474627657,
0.6894193489329519,
0.6894128515825795,
0.6894063554110188,
0.6893998604176417,
0.6893933666018192,
0.6893868739629238,
0.6893803825003278,
0.6893738922134036,
0.6893674031015249,
0.689360915164065,
0.6893544284003981,
0.6893479428098981,
0.6893414583919399,
0.6893349751458988,
0.6893284930711506,
0.6893220121670708,
0.6893155324330359,
0.6893090538684223,
0.6893025764726077,
0.6892961002449691,
0.6892896251848848,
0.6892831512917327,
0.6892766785648914,
0.68927020700374,
0.6892637366076584,
0.6892572673760258,
0.689250799308223,
0.6892443324036301,
0.6892378666616283,
0.689231402081599,
0.6892249386629243,
0.6892184764049858,
0.6892120153071661,
0.6892055553688485,
0.689199096589416,
0.6891926389682527,
0.689186182504742,
0.6891797271982693,
0.6891732730482184,
0.6891668200539756,
0.6891603682149255,
0.6891539175304552,
0.6891474679999502,
0.6891410196227977,
0.6891345723983848,
0.6891281263260991,
0.6891216814053285,
0.6891152376354615,
0.6891087950158865,
0.6891023535459927,
0.6890959132251698,
0.6890894740528071,
0.6890830360282953,
0.689076599151025,
0.6890701634203871,
0.6890637288357729,
0.6890572953965745,
0.6890508631021836,
0.6890444319519929,
0.6890380019453953,
0.6890315730817844,
0.6890251453605535,
0.6890187187810966,
0.689012293342808,
0.6890058690450832,
0.688999445887317,
0.6889930238689047,
0.6889866029892425,
0.6889801832477265,
0.6889737646437539,
0.6889673471767213,
0.6889609308460263,
0.6889545156510667,
0.6889481015912406,
0.688941688665947,
0.6889352768745843,
0.6889288662165526,
0.6889224566912505,
0.6889160482980794,
0.6889096410364388,
0.6889032349057299,
0.6888968299053541,
0.6888904260347121,
0.688884023293207,
0.6888776216802407,
0.6888712211952157,
0.6888648218375353,
0.688858423606603,
0.6888520265018226,
0.6888456305225984,
0.6888392356683349,
0.6888328419384366,
0.6888264493323096,
0.6888200578493595,
0.6888136674889918,
0.6888072782506132,
0.6888008901336309,
0.6887945031374517,
0.6887881172614835,
0.6887817325051336,
0.6887753488678109,
0.6887689663489238,
0.6887625849478818,
0.6887562046640934,
0.6887498254969691,
0.6887434474459192,
0.6887370705103535,
0.6887306946896832,
0.6887243199833197,
0.6887179463906749,
0.6887115739111601,
0.6887052025441883,
0.688698832289172,
0.6886924631455241,
0.6886860951126584,
0.6886797281899885,
0.6886733623769289,
0.6886669976728934,
0.688660634077298,
0.6886542715895576,
0.6886479102090873,
0.6886415499353039,
0.6886351907676235,
0.6886288327054628,
0.688622475748239,
0.6886161198953701,
0.6886097651462728,
0.6886034115003662,
0.6885970589570687,
0.6885907075157992,
0.6885843571759768,
0.688578007937022,
0.688571659798354,
0.6885653127593935,
0.6885589668195611,
0.688552621978278,
0.6885462782349664,
0.6885399355890471,
0.6885335940399426,
0.688527253587076,
0.6885209142298696,
0.6885145759677471,
0.6885082388001322,
0.6885019027264487,
0.688495567746121,
0.6884892338585739,
0.6884829010632325,
0.6884765693595224,
0.6884702387468692,
0.6884639092246994,
0.688457580792439,
0.6884512534495155,
0.688444927195356,
0.6884386020293876,
0.688432277951039,
0.6884259549597384,
0.6884196330549139,
0.6884133122359953,
0.6884069925024113,
0.6884006738535923,
0.6883943562889684,
0.6883880398079698,
0.6883817244100272,
0.6883754100945724,
0.6883690968610363,
0.6883627847088507,
0.6883564736374485,
0.6883501636462622,
0.6883438547347244,
0.6883375469022686,
0.6883312401483285,
0.6883249344723381,
0.6883186298737317,
0.6883123263519442,
0.6883060239064109,
0.6882997225365669,
0.688293422241848,
0.6882871230216905,
0.688280824875531,
0.688274527802806,
0.6882682318029532,
0.6882619368754096,
0.6882556430196136,
0.6882493502350031,
0.6882430585210174,
0.6882367678770945,
0.6882304783026743,
0.6882241897971964,
0.6882179023601008,
0.688211615990828,
0.6882053306888185,
0.6881990464535135,
0.6881927632843541,
0.6881864811807826,
0.688180200142241,
0.6881739201681716,
0.6881676412580173,
0.688161363411221,
0.6881550866272266,
0.688148810905478,
0.6881425362454192,
0.6881362626464946,
0.6881299901081493,
0.6881237186298288,
0.6881174482109782,
0.688111178851044,
0.6881049105494719,
0.6880986433057091,
0.6880923771192019,
0.6880861119893985,
0.6880798479157462,
0.6880735848976928,
0.6880673229346868,
0.6880610620261772,
0.6880548021716124,
0.6880485433704425,
0.6880422856221169,
0.688036028926086,
0.6880297732817994,
0.688023518688709,
0.6880172651462653,
0.6880110126539196,
0.6880047612111242,
0.6879985108173311,
0.6879922614719927,
0.6879860131745617,
0.6879797659244915,
0.6879735197212354,
0.6879672745642478,
0.6879610304529822,
0.6879547873868935,
0.6879485453654369,
0.6879423043880668,
0.6879360644542397,
0.6879298255634108,
0.6879235877150365,
0.687917350908574,
0.6879111151434792,
0.68790488041921,
0.6878986467352243,
0.6878924140909791,
0.6878861824859337,
0.6878799519195458,
0.6878737223912752,
0.6878674939005806,
0.687861266446922,
0.687855040029759,
0.6878488146485524,
0.6878425903027628,
0.6878363669918508,
0.6878301447152777,
0.6878239234725055,
0.6878177032629961,
0.6878114840862118,
0.6878052659416156,
0.6877990488286697,
0.6877928327468382,
0.6877866176955842,
0.6877804036743725,
0.6877741906826668,
0.6877679787199319,
0.6877617677856327,
0.6877555578792349,
0.6877493490002041,
0.687743141148006,
0.6877369343221074,
0.6877307285219747,
0.687724523747075,
0.6877183199968756,
0.6877121172708444,
0.6877059155684492,
0.6876997148891582,
0.6876935152324404,
0.6876873165977645,
0.6876811189846005,
0.6876749223924173,
0.6876687268206851,
0.6876625322688749,
0.6876563387364565,
0.6876501462229011,
0.6876439547276804,
0.6876377642502658,
0.6876315747901297,
0.6876253863467437,
0.6876191989195807,
0.6876130125081141,
0.6876068271118165,
0.6876006427301624,
0.6875944593626252,
0.6875882770086794,
0.6875820956677994,
0.6875759153394605,
0.6875697360231376,
0.6875635577183067,
0.6875573804244435,
0.6875512041410243,
0.6875450288675257,
0.6875388546034246,
0.6875326813481982,
0.6875265091013244,
0.6875203378622806,
0.6875141676305453,
0.6875079984055974,
0.6875018301869149,
0.6874956629739777,
0.6874894967662654,
0.687483331563257,
0.6874771673644338,
0.6874710041692756,
0.6874648419772635,
0.6874586807878786,
0.6874525206006021,
0.6874463614149161,
0.6874402032303029,
0.6874340460462444,
0.6874278898622237,
0.6874217346777238,
0.6874155804922284,
0.6874094273052211,
0.6874032751161857,
0.687397123924607,
0.6873909737299688,
0.6873848245317573,
0.6873786763294574,
0.6873725291225545,
0.6873663829105352,
0.6873602376928849,
0.687354093469091,
0.6873479502386403,
0.6873418080010198,
0.6873356667557176,
0.6873295265022208,
0.6873233872400182,
0.6873172489685984,
0.6873111116874501,
0.6873049753960627,
0.6872988400939255,
0.6872927057805283,
0.6872865724553615,
0.6872804401179151,
0.68727430876768,
0.6872681784041478,
0.6872620490268097,
0.687255920635157,
0.6872497932286825,
0.6872436668068779,
0.6872375413692363,
0.68723141691525,
0.6872252934444134,
0.6872191709562195,
0.6872130494501623,
0.6872069289257362,
0.6872008093824358,
0.6871946908197555,
0.6871885732371911,
0.6871824566342382,
0.6871763410103922,
0.6871702263651496,
0.6871641126980065,
0.6871580000084602,
0.6871518882960073,
0.6871457775601456,
0.6871396678003725,
0.6871335590161864,
0.6871274512070853,
0.6871213443725684,
0.6871152385121341,
0.6871091336252817,
0.6871030297115114,
0.6870969267703225,
0.6870908248012159,
0.6870847238036913,
0.68707862377725,
0.6870725247213932,
0.6870664266356225,
0.6870603295194394,
0.6870542333723462,
0.6870481381938451,
0.6870420439834392,
0.6870359507406308,
0.687029858464924,
0.6870237671558223,
0.6870176768128294,
0.6870115874354497,
0.6870054990231881,
0.6869994115755486,
0.6869933250920375,
0.6869872395721597,
0.6869811550154208,
0.6869750714213273,
0.6869689887893855,
0.6869629071191022,
0.6869568264099849,
0.6869507466615399,
0.6869446678732757,
0.6869385900446999,
0.6869325131753212,
0.6869264372646477,
0.6869203623121884,
0.6869142883174523,
0.6869082152799498,
0.6869021431991895,
0.6868960720746824,
0.6868900019059384,
0.6868839326924686,
0.6868778644337838,
0.6868717971293953,
0.686865730778815,
0.6868596653815545,
0.6868536009371263,
0.6868475374450429,
0.6868414749048171,
0.6868354133159618,
0.6868293526779911,
0.6868232929904182,
0.6868172342527574,
0.6868111764645234,
0.68680511962523,
0.6867990637343928,
0.6867930087915272,
0.6867869547961485,
0.6867809017477724,
0.6867748496459155,
0.686768798490094,
0.6867627482798252,
0.6867566990146253,
0.6867506506940125,
0.6867446033175041,
0.686738556884618,
0.6867325113948729,
0.686726466847787,
0.6867204232428794,
0.6867143805796693,
0.6867083388576762,
0.6867022980764199,
0.6866962582354202,
0.6866902193341977,
0.6866841813722733,
0.6866781443491677,
0.6866721082644022,
0.6866660731174986,
0.6866600389079782,
0.686654005635364,
0.6866479732991783,
0.6866419418989436,
0.6866359114341828,
0.6866298819044195,
0.6866238533091775,
0.686617825647981,
0.6866117989203535,
0.6866057731258198,
0.6865997482639055,
0.686593724334135,
0.6865877013360339,
0.686581679269128,
0.6865756581329433,
0.686569637927006,
...])
w.shape
(14, 1)
cost_lst[iter_num-1]
0.365635134439536
#绘制误差曲线
plt.plot(range(iter_num),cost_lst,"b-o")
[<matplotlib.lines.Line2D at 0x1422d44cdc0>]
这时要重新绘图了
items
X2
array([[ 1.00000000e+00, 6.99560000e-01, 4.89384194e-01, ...,
1.28625106e-03, 1.34745327e-04, 9.42624411e-05],
[ 1.00000000e+00, 6.84940000e-01, 4.69142804e-01, ...,
4.03513411e-03, -7.97681228e-04, -5.46363780e-04],
[ 1.00000000e+00, 6.92250000e-01, 4.79210063e-01, ...,
2.18864648e-02, -9.76055545e-03, -6.75674451e-03],
...,
[ 1.00000000e+00, 9.99270000e-01, 9.98540533e-01, ...,
2.34349278e-01, -1.13696444e-01, -1.13613445e-01],
[ 1.00000000e+00, 9.99270000e-01, 9.98540533e-01, ...,
4.00913674e-05, -2.54406238e-07, -2.54220521e-07],
[ 1.00000000e+00, -3.06120000e-02, 9.37094544e-04, ...,
3.75068364e-04, 2.53215646e-01, -7.75143736e-03]])
X2.shape,w.shape
((118, 14), (14, 1))
y_pred=[1 if item>=0.5 else 0 for item in sigmoid(X2@w).ravel()]
y_pred=np.array(y_pred)
np.sum(y_pred==y2)/y2.shape[0]
0.8305084745762712
2.6 定义正则化项的代价函数
regularized cost（正则化代价函数）
J ( w ) = 1 m ∑ i = 1 m [ − y ( i ) log ( h ( x ( i ) ) ) − ( 1 − y ( i ) ) log ( 1 − h ( x ( i ) ) ) ] + λ 2 m ∑ j = 1 n w j 2 J\left( w \right)=\frac{1}{m}\sum\limits_{i=1}^{m}{[-{ {y}^{(i)}}\log \left( { {h}}\left( { {x}^{(i)}} \right) \right)-\left( 1-{ {y}^{(i)}} \right)\log \left( 1-{ {h}}\left( { {x}^{(i)}} \right) \right)]}+\frac{\lambda }{2m}\sum\limits_{j=1}^{n}{w _{j}^{2}} J(w)=m1i=1∑m[−y(i)log(h(x(i)))−(1−y(i))log(1−h(x(i)))]+2mλj=1∑nwj2
w[:,0]
array([ 3.03503577, 3.20158942, -4.0495866 , -1.04983379, -3.95636068,
2.0490215 , -3.40302089, -0.79821365, -1.23393575, -7.32541507,
-1.41115593, -1.80717912, -0.54355034, 0.11775491])
#代价函数构造
def cost_reg(X,w,y,lambd):
#当X(m,n+1),y(m,),w(n+1,1)
y_hat=sigmoid(X@w)
right1=np.multiply(y.ravel(),np.log(y_hat).ravel())+np.multiply((1-y).ravel(),np.log(1-y_hat).ravel())
right2=(lambd/(2*X.shape[0]))*np.sum(np.power(w[1:,0],2))
cost=-np.sum(right1)/X.shape[0]+right2
return cost
cost(X2,w,y2)
0.365635134439536
lambd=2
cost_reg(X2,w,y2,lambd)
1.3874260376493517
2.7 定义正则化的梯度下降算法
如果我们要使用梯度下降法令这个代价函数最小化，因为我们未对 w 0 { {w }_{0}} w0 进行正则化，所以梯度下降算法将分两种情形：
KaTeX parse error: No such environment: align at position 7: \begin{̲a̲l̲i̲g̲n̲}̲ & 重复\text{ }…
对上面的算法中 j=1,2,…,n 时的更新式子进行调整可得：
w j : = w j ( 1 − a λ m ) − a 1 m ∑ i = 1 m ( h w ( x ( i ) ) − y ( i ) ) x j ( i ) {
{w }_{j}}:={
{w }_{j}}(1-a\frac{\lambda }{m})-a\frac{1}{m}\sum\limits_{i=1}^{m}{({
{h}_{w }}\left( {
{x}^{(i)}} \right)-{
{y}^{(i)}})x_{j}^{(i)}} wj:=wj(1−amλ)−am1i=1∑m(hw(x(i))−y(i))xj(i)
def grandient_reg(X,w,y,iter_num,alpha,lambd):
y=y.reshape((X.shape[0],1))
w=np.zeros((X.shape[1],1))
cost_lst=[]
for i in range(iter_num):
y_pred=h(X,w)-y
temp=np.zeros((X.shape[1],1))
for j in range(0,X.shape[1]):
if j==0:
right_0=np.multiply(y_pred.ravel(),X[:,0])
gradient_0=1/(X.shape[0])*(np.sum(right_0))
temp[j,0]=w[j,0]-alpha*(gradient_0)
else:
right=np.multiply(y_pred.ravel(),X[:,j])
reg=(lambd/X.shape[0])*w[j,0]
gradient=1/(X.shape[0])*(np.sum(right))
temp[j,0]=w[j,0]-alpha*(gradient+reg)
w=temp
cost_lst.append(cost_reg(X,w,y,lambd))
return w,cost_lst
iter_num,alpha,lambd=600000,0.001,1
w2,cost_lst=grandient_reg(X2,w,y2,iter_num,alpha,lambd)
plt.plot(range(iter_num),cost_lst)
[<matplotlib.lines.Line2D at 0x1422dddef40>]
请注意等式中的"reg" 项。还注意到另外的一个“学习率”参数。这是一种超参数，用来控制正则化项。现在我们需要添加正则化梯度函数：
就像在第一部分中做的一样，初始化变量。
实验1 计算基于正则化得到的准确率
y_pred=[1 if item>=0.5 else 0 for item in sigmoid(X2@w).ravel()]
y_pred=np.array(y_pred)
np.sum(y_pred==y2)/y2.shape[0]
0.8305084745762712
现在，让我们尝试调用新的默认为0的 w w w的正则化函数，以确保计算工作正常。最后，我们可以使用第1部分中的预测函数来查看我们的方案在训练数据上的准确度。
2.8 试试sklearn
from sklearn import linear_model#调用sklearn的线性回归包
model = linear_model.LogisticRegression(penalty='l2', C=1.0)
model.fit(X2, y2.ravel())
LogisticRegression()
model.score(X2, y2)
0.8389830508474576
参考
[1] Andrew Ng. Machine Learning[EB/OL]. StanfordUniversity,2014.https://www.coursera.org/course/ml
[2] 李航. 统计学习方法[M]. 北京: 清华大学出版社,2019.
import sklearn.datasets as datasets
from sklearn.linear_model import LogisticRegression
import matplotlib.pyplot as plt
3.1 准备数据
X, y = datasets.make_blobs(n_samples=200, n_features=2, centers=2, random_state=0)
X.shape, y.shape
((200, 2), (200,))
X
array([[ 2.8219307 , 1.25395648],
[ 1.65581849, 1.26771955],
[ 3.12377692, 0.44427786],
[ 1.4178305 , 0.50039185],
[ 2.50904929, 5.7731461 ],
[ 0.30380963, 3.94423417],
[ 1.12031365, 5.75806083],
[ 0.08848433, 2.32299086],
[ 1.92238694, 0.59987278],
[-0.65392827, 4.76656958],
[ 1.45895348, 0.84509636],
[ 0.51447051, 0.96092565],
[ 1.35269561, 3.20438654],
[-0.27652528, 5.08127768],
[ 2.15299249, 1.48061734],
[ 0.17286041, 3.61423755],
[-0.20029671, -0.12484318],
[ 3.52184624, 1.7502156 ],
[ 2.5763324 , 0.32187569],
[ 2.89689879, 0.64820508],
[ 1.36742991, -0.31641374],
[-0.33963733, 3.84220272],
[ 2.07592967, 4.95905106],
[ 0.206354 , 4.84303652],
[ 2.89921211, 5.78430212],
[ 0.340424 , 4.98022062],
[ 1.78753398, -0.23034767],
[ 1.18454506, 5.28042636],
[ 1.61434489, 0.61730816],
[-0.60390472, 1.50398318],
[-0.19685333, 6.24740851],
[ 0.72100905, -0.44905385],
[ 2.96544643, 1.21488188],
[ 1.06975678, -0.57417135],
[ 0.90802847, 6.01713005],
[-0.17119857, 3.86596728],
[ 1.36321767, 2.43404071],
[ 1.24190326, -0.56876067],
[ 1.33263648, 5.0103605 ],
[ 0.62835793, 4.4601363 ],
[ 0.70826671, 5.10624372],
[ 2.8285205 , -0.28621698],
[ 1.57561171, 1.51802196],
[ 0.94808785, 4.7321192 ],
[ 1.0427873 , 4.60625923],
[ 2.19722068, 0.57833524],
[-0.29421492, 5.27318404],
[ 0.02458305, 2.96215652],
[ 2.16429987, 4.62072994],
[ 4.31457647, 0.85540651],
[ 0.86640826, 0.39084731],
[ 1.5528609 , 4.09548857],
[ 1.44193252, 2.76754364],
[ 0.93698726, 3.13569383],
[ 2.21177406, 1.1298447 ],
[ 0.46546494, 3.12315514],
[ 3.13950603, 5.64031528],
[ 0.9867701 , 6.08965782],
[ 1.74438135, 0.99506383],
[ 0.89791226, 0.58537141],
[ 2.74904067, 0.73809022],
[ 4.01117983, 1.28775698],
[-0.09448254, 5.35823905],
[ 0.62227617, 2.92883603],
[ 3.35941485, 5.24826681],
[ 2.1047625 , 1.39150044],
[ 1.01001416, 2.10880895],
[ 2.63378902, 1.24731812],
[ 2.15504965, 4.12386249],
[ 0.28170222, 4.15415279],
[ 4.35918422, -0.16235216],
[ 0.4666179 , 3.86571303],
[ 0.11898772, 1.08644226],
[ 1.69057398, 1.05436752],
[ 1.92156596, 1.97540747],
[ 2.84159548, 0.43124456],
[ 1.89760051, 3.15438716],
[ 0.74874067, 2.55579434],
[ 0.1631238 , 2.57750473],
[ 1.45661358, -0.21823333],
[ 1.14294357, 4.93881876],
[ 2.03824711, 1.2768154 ],
[-1.57671974, 4.95740592],
[-0.73000011, 6.25456272],
[ 1.37125662, 2.55721446],
[ 2.84382904, 5.20983199],
[-0.51498751, 4.74317903],
[ 2.01309607, 0.61077647],
[ 1.67038771, 0.99201525],
[ 1.59167155, 1.37914513],
[ 1.37861172, 3.61897724],
[-0.02394527, 2.75901623],
[ 0.11504439, 6.21385228],
[ 2.11567076, 3.06896151],
[ 1.91931782, 2.03455502],
[ 2.03958541, 1.05859183],
[ 1.84836385, 1.77784257],
[ 0.52073758, 4.32126649],
[ 1.0220286 , 4.11660348],
[ 1.2911236 , -0.54012781],
[ 0.34194798, 3.94104616],
[ 2.5490093 , 0.78155972],
[ 1.15369622, 3.90200639],
[ 0.60708824, 4.06440815],
[-0.63762777, 4.09104705],
[ 1.28933778, 3.44969159],
[-0.12811326, 4.35595241],
[ 0.08080352, 4.69068983],
[ 3.20759909, 1.97728225],
[ 0.06344785, 5.42080362],
[ 2.80245586, -0.2912813 ],
[ 2.20656076, 5.50616718],
[ 1.7373078 , 4.42546234],
[ 1.70536064, 4.43277024],
[ 0.47823763, 6.23331938],
[ 2.6225578 , 0.67498856],
[ 0.21219797, 0.41968966],
[ 1.76343016, 0.13617145],
[ 1.09932252, 0.55168188],
[ 1.86461403, 0.50281415],
[ 1.59034945, 5.225994 ],
[ 2.48152625, 1.57457169],
[ 0.58894326, 4.00148458],
[ 1.35056725, 1.84092438],
[ 0.3571617 , 1.28494414],
[ 2.7216506 , 0.43694387],
[ 1.92352205, 4.14877723],
[ 2.0309414 , 0.15963275],
[ 2.69858199, -0.67295975],
[ 1.83310069, 3.65276173],
[ 1.45795145, 0.65974193],
[ 1.37227679, 3.21072582],
[ 0.54111653, 6.15305106],
[ 2.57915855, 0.98608575],
[ 0.23151526, 3.47734879],
[ 2.84382807, 3.32650945],
[-0.24916544, 5.1481503 ],
[ 1.40285894, 0.50671028],
[ 2.74508569, 2.19950989],
[ 3.70340245, 1.06189142],
[ 1.42013331, 4.63746165],
[ 0.47232912, 1.50804304],
[ 1.8971289 , 4.62251498],
[ 0.10547293, 3.72493766],
[ 2.32978388, 0.00674858],
[ 1.60150153, 2.70172967],
[ 0.30193742, 4.33561789],
[-0.31658683, 4.5708382 ],
[ 2.34161121, 1.50650749],
[ 1.94472686, 1.91783637],
[ 1.40297392, 0.37647435],
[ 0.06897171, 4.35573272],
[ 1.74806063, 5.12729148],
[ 1.49954674, 4.132241 ],
[ 0.63120661, 0.40434378],
[ 1.27450825, 5.63017322],
[ 0.66471755, 4.35995267],
[ 1.42717996, 0.41663654],
[ 2.9871159 , 1.23762864],
[ 1.33566313, 0.08467067],
[ 0.92844171, 0.16698591],
[ 2.46452227, 6.1996765 ],
[ 2.85942078, 2.95602827],
[ 2.69539905, -0.71929238],
[ 1.70183577, -0.71881053],
[ 1.11082127, 0.48761397],
[ 0.23670708, 5.84680192],
[ 1.1312175 , 4.68194985],
[ 0.33265168, 2.08038418],
[-0.07228289, 2.88376939],
[ 1.74625455, -0.77834015],
[ 1.93710348, 0.21748546],
[ 3.41979937, 0.20821448],
[ 1.10318217, 4.70577669],
[ 2.33570923, -0.09545995],
[ 1.64856484, 4.71124916],
[ 1.92569089, 4.39133857],
[ 0.57309313, 5.5262324 ],
[ 3.54975207, -1.17232137],
[ 2.45431387, -1.8749291 ],
[ 0.89908509, 1.67886176],
[ 1.84070628, 3.56162231],
[ 1.99364112, 0.79035838],
[ 2.102906 , 3.22385582],
[ 0.87305123, 4.71438583],
[ 0.5626511 , 3.55633252],
[ 2.75372467, 0.90143455],
[ 2.09389807, -0.75905144],
[ 1.32967014, -0.4857003 ],
[-0.05797276, 4.98538185],
[ 1.51240605, 1.31371371],
[ 0.87781755, 3.64030904],
[ 0.29937694, 1.34859812],
[ 2.33519212, 0.79951327],
[ 2.91319145, 2.03876553],
[ 2.74680627, 1.5924128 ],
[ 2.47034915, 4.09862906],
[ 3.2460247 , 2.84942165],
[ 1.9263585 , 4.15243012],
[-0.18887976, 5.20461381]])
plt.scatter(X[:, 0], X[:, 1], c=y)
<matplotlib.collections.PathCollection at 0x142327368e0>
实验2 完成3.2 调用逻辑回归模型完成分类
3.2 调用普通的逻辑回归模型来进行多分类(调用1.4的梯度下降算法)
X=np.insert(X,0,1,axis=1)
X
array([[ 1. , 2.8219307 , 1.25395648],
[ 1. , 1.65581849, 1.26771955],
[ 1. , 3.12377692, 0.44427786],
[ 1. , 1.4178305 , 0.50039185],
[ 1. , 2.50904929, 5.7731461 ],
[ 1. , 0.30380963, 3.94423417],
[ 1. , 1.12031365, 5.75806083],
[ 1. , 0.08848433, 2.32299086],
[ 1. , 1.92238694, 0.59987278],
[ 1. , -0.65392827, 4.76656958],
[ 1. , 1.45895348, 0.84509636],
[ 1. , 0.51447051, 0.96092565],
[ 1. , 1.35269561, 3.20438654],
[ 1. , -0.27652528, 5.08127768],
[ 1. , 2.15299249, 1.48061734],
[ 1. , 0.17286041, 3.61423755],
[ 1. , -0.20029671, -0.12484318],
[ 1. , 3.52184624, 1.7502156 ],
[ 1. , 2.5763324 , 0.32187569],
[ 1. , 2.89689879, 0.64820508],
[ 1. , 1.36742991, -0.31641374],
[ 1. , -0.33963733, 3.84220272],
[ 1. , 2.07592967, 4.95905106],
[ 1. , 0.206354 , 4.84303652],
[ 1. , 2.89921211, 5.78430212],
[ 1. , 0.340424 , 4.98022062],
[ 1. , 1.78753398, -0.23034767],
[ 1. , 1.18454506, 5.28042636],
[ 1. , 1.61434489, 0.61730816],
[ 1. , -0.60390472, 1.50398318],
[ 1. , -0.19685333, 6.24740851],
[ 1. , 0.72100905, -0.44905385],
[ 1. , 2.96544643, 1.21488188],
[ 1. , 1.06975678, -0.57417135],
[ 1. , 0.90802847, 6.01713005],
[ 1. , -0.17119857, 3.86596728],
[ 1. , 1.36321767, 2.43404071],
[ 1. , 1.24190326, -0.56876067],
[ 1. , 1.33263648, 5.0103605 ],
[ 1. , 0.62835793, 4.4601363 ],
[ 1. , 0.70826671, 5.10624372],
[ 1. , 2.8285205 , -0.28621698],
[ 1. , 1.57561171, 1.51802196],
[ 1. , 0.94808785, 4.7321192 ],
[ 1. , 1.0427873 , 4.60625923],
[ 1. , 2.19722068, 0.57833524],
[ 1. , -0.29421492, 5.27318404],
[ 1. , 0.02458305, 2.96215652],
[ 1. , 2.16429987, 4.62072994],
[ 1. , 4.31457647, 0.85540651],
[ 1. , 0.86640826, 0.39084731],
[ 1. , 1.5528609 , 4.09548857],
[ 1. , 1.44193252, 2.76754364],
[ 1. , 0.93698726, 3.13569383],
[ 1. , 2.21177406, 1.1298447 ],
[ 1. , 0.46546494, 3.12315514],
[ 1. , 3.13950603, 5.64031528],
[ 1. , 0.9867701 , 6.08965782],
[ 1. , 1.74438135, 0.99506383],
[ 1. , 0.89791226, 0.58537141],
[ 1. , 2.74904067, 0.73809022],
[ 1. , 4.01117983, 1.28775698],
[ 1. , -0.09448254, 5.35823905],
[ 1. , 0.62227617, 2.92883603],
[ 1. , 3.35941485, 5.24826681],
[ 1. , 2.1047625 , 1.39150044],
[ 1. , 1.01001416, 2.10880895],
[ 1. , 2.63378902, 1.24731812],
[ 1. , 2.15504965, 4.12386249],
[ 1. , 0.28170222, 4.15415279],
[ 1. , 4.35918422, -0.16235216],
[ 1. , 0.4666179 , 3.86571303],
[ 1. , 0.11898772, 1.08644226],
[ 1. , 1.69057398, 1.05436752],
[ 1. , 1.92156596, 1.97540747],
[ 1. , 2.84159548, 0.43124456],
[ 1. , 1.89760051, 3.15438716],
[ 1. , 0.74874067, 2.55579434],
[ 1. , 0.1631238 , 2.57750473],
[ 1. , 1.45661358, -0.21823333],
[ 1. , 1.14294357, 4.93881876],
[ 1. , 2.03824711, 1.2768154 ],
[ 1. , -1.57671974, 4.95740592],
[ 1. , -0.73000011, 6.25456272],
[ 1. , 1.37125662, 2.55721446],
[ 1. , 2.84382904, 5.20983199],
[ 1. , -0.51498751, 4.74317903],
[ 1. , 2.01309607, 0.61077647],
[ 1. , 1.67038771, 0.99201525],
[ 1. , 1.59167155, 1.37914513],
[ 1. , 1.37861172, 3.61897724],
[ 1. , -0.02394527, 2.75901623],
[ 1. , 0.11504439, 6.21385228],
[ 1. , 2.11567076, 3.06896151],
[ 1. , 1.91931782, 2.03455502],
[ 1. , 2.03958541, 1.05859183],
[ 1. , 1.84836385, 1.77784257],
[ 1. , 0.52073758, 4.32126649],
[ 1. , 1.0220286 , 4.11660348],
[ 1. , 1.2911236 , -0.54012781],
[ 1. , 0.34194798, 3.94104616],
[ 1. , 2.5490093 , 0.78155972],
[ 1. , 1.15369622, 3.90200639],
[ 1. , 0.60708824, 4.06440815],
[ 1. , -0.63762777, 4.09104705],
[ 1. , 1.28933778, 3.44969159],
[ 1. , -0.12811326, 4.35595241],
[ 1. , 0.08080352, 4.69068983],
[ 1. , 3.20759909, 1.97728225],
[ 1. , 0.06344785, 5.42080362],
[ 1. , 2.80245586, -0.2912813 ],
[ 1. , 2.20656076, 5.50616718],
[ 1. , 1.7373078 , 4.42546234],
[ 1. , 1.70536064, 4.43277024],
[ 1. , 0.47823763, 6.23331938],
[ 1. , 2.6225578 , 0.67498856],
[ 1. , 0.21219797, 0.41968966],
[ 1. , 1.76343016, 0.13617145],
[ 1. , 1.09932252, 0.55168188],
[ 1. , 1.86461403, 0.50281415],
[ 1. , 1.59034945, 5.225994 ],
[ 1. , 2.48152625, 1.57457169],
[ 1. , 0.58894326, 4.00148458],
[ 1. , 1.35056725, 1.84092438],
[ 1. , 0.3571617 , 1.28494414],
[ 1. , 2.7216506 , 0.43694387],
[ 1. , 1.92352205, 4.14877723],
[ 1. , 2.0309414 , 0.15963275],
[ 1. , 2.69858199, -0.67295975],
[ 1. , 1.83310069, 3.65276173],
[ 1. , 1.45795145, 0.65974193],
[ 1. , 1.37227679, 3.21072582],
[ 1. , 0.54111653, 6.15305106],
[ 1. , 2.57915855, 0.98608575],
[ 1. , 0.23151526, 3.47734879],
[ 1. , 2.84382807, 3.32650945],
[ 1. , -0.24916544, 5.1481503 ],
[ 1. , 1.40285894, 0.50671028],
[ 1. , 2.74508569, 2.19950989],
[ 1. , 3.70340245, 1.06189142],
[ 1. , 1.42013331, 4.63746165],
[ 1. , 0.47232912, 1.50804304],
[ 1. , 1.8971289 , 4.62251498],
[ 1. , 0.10547293, 3.72493766],
[ 1. , 2.32978388, 0.00674858],
[ 1. , 1.60150153, 2.70172967],
[ 1. , 0.30193742, 4.33561789],
[ 1. , -0.31658683, 4.5708382 ],
[ 1. , 2.34161121, 1.50650749],
[ 1. , 1.94472686, 1.91783637],
[ 1. , 1.40297392, 0.37647435],
[ 1. , 0.06897171, 4.35573272],
[ 1. , 1.74806063, 5.12729148],
[ 1. , 1.49954674, 4.132241 ],
[ 1. , 0.63120661, 0.40434378],
[ 1. , 1.27450825, 5.63017322],
[ 1. , 0.66471755, 4.35995267],
[ 1. , 1.42717996, 0.41663654],
[ 1. , 2.9871159 , 1.23762864],
[ 1. , 1.33566313, 0.08467067],
[ 1. , 0.92844171, 0.16698591],
[ 1. , 2.46452227, 6.1996765 ],
[ 1. , 2.85942078, 2.95602827],
[ 1. , 2.69539905, -0.71929238],
[ 1. , 1.70183577, -0.71881053],
[ 1. , 1.11082127, 0.48761397],
[ 1. , 0.23670708, 5.84680192],
[ 1. , 1.1312175 , 4.68194985],
[ 1. , 0.33265168, 2.08038418],
[ 1. , -0.07228289, 2.88376939],
[ 1. , 1.74625455, -0.77834015],
[ 1. , 1.93710348, 0.21748546],
[ 1. , 3.41979937, 0.20821448],
[ 1. , 1.10318217, 4.70577669],
[ 1. , 2.33570923, -0.09545995],
[ 1. , 1.64856484, 4.71124916],
[ 1. , 1.92569089, 4.39133857],
[ 1. , 0.57309313, 5.5262324 ],
[ 1. , 3.54975207, -1.17232137],
[ 1. , 2.45431387, -1.8749291 ],
[ 1. , 0.89908509, 1.67886176],
[ 1. , 1.84070628, 3.56162231],
[ 1. , 1.99364112, 0.79035838],
[ 1. , 2.102906 , 3.22385582],
[ 1. , 0.87305123, 4.71438583],
[ 1. , 0.5626511 , 3.55633252],
[ 1. , 2.75372467, 0.90143455],
[ 1. , 2.09389807, -0.75905144],
[ 1. , 1.32967014, -0.4857003 ],
[ 1. , -0.05797276, 4.98538185],
[ 1. , 1.51240605, 1.31371371],
[ 1. , 0.87781755, 3.64030904],
[ 1. , 0.29937694, 1.34859812],
[ 1. , 2.33519212, 0.79951327],
[ 1. , 2.91319145, 2.03876553],
[ 1. , 2.74680627, 1.5924128 ],
[ 1. , 2.47034915, 4.09862906],
[ 1. , 3.2460247 , 2.84942165],
[ 1. , 1.9263585 , 4.15243012],
[ 1. , -0.18887976, 5.20461381]])
#调用梯度下降算法
iter_num,alpha=600000,0.001
w,cost_lst=grandient(X,y,iter_num,alpha)
#绘制误差曲线
plt.plot(range(iter_num),cost_lst,"b-o")
[<matplotlib.lines.Line2D at 0x1423849dc70>]
X[y==0,1]
array([ 2.50904929, 0.30380963, 1.12031365, 0.08848433, -0.65392827,
1.35269561, -0.27652528, 0.17286041, -0.33963733, 2.07592967,
0.206354 , 2.89921211, 0.340424 , 1.18454506, -0.19685333,
0.90802847, -0.17119857, 1.33263648, 0.62835793, 0.70826671,
0.94808785, 1.0427873 , -0.29421492, 2.16429987, 1.5528609 ,
1.44193252, 0.93698726, 0.46546494, 3.13950603, 0.9867701 ,
-0.09448254, 0.62227617, 3.35941485, 2.15504965, 0.28170222,
0.4666179 , 0.1631238 , 1.14294357, -1.57671974, -0.73000011,
2.84382904, -0.51498751, 1.37861172, -0.02394527, 0.11504439,
2.11567076, 0.52073758, 1.0220286 , 0.34194798, 1.15369622,
0.60708824, -0.63762777, 1.28933778, -0.12811326, 0.08080352,
0.06344785, 2.20656076, 1.7373078 , 1.70536064, 0.47823763,
1.59034945, 0.58894326, 1.92352205, 1.83310069, 1.37227679,
0.54111653, 0.23151526, 2.84382807, -0.24916544, 1.42013331,
1.8971289 , 0.10547293, 1.60150153, 0.30193742, -0.31658683,
0.06897171, 1.74806063, 1.49954674, 1.27450825, 0.66471755,
2.46452227, 2.85942078, 0.23670708, 1.1312175 , 0.33265168,
-0.07228289, 1.10318217, 1.64856484, 1.92569089, 0.57309313,
1.84070628, 2.102906 , 0.87305123, 0.5626511 , -0.05797276,
0.87781755, 2.47034915, 3.2460247 , 1.9263585 , -0.18887976])
#绘制线性的决策边界
x_exmal=np.linspace(np.min(X[:,1]),np.max(X[:,1]),50)
x2=(-w[0,0]-w[1,0]*x_exmal)/(w[2,0])
plt.plot(x_exmal,x2,"r-o")
plt.scatter(X[y==1,1],X[y==1,2],color="b",marker="o")
plt.scatter(X[y==0,1],X[y==0,2],color="c",marker="^")
plt.show()
#计算准确率
y_pred=[1 if item>=0.5 else 0 for item in sigmoid(X@w).ravel()]
y_pred=np.array(y_pred)
np.sum(y_pred==y)/y.shape[0]
0.97
实验3 完成3.3 调用正则化的逻辑回归模型完成分类
3.3调用正则化的逻辑回归模型来进行多分类(调用2.7的梯度下降算法)
y.shape,X.shape,w.shape
((200,), (200, 3), (3, 1))
#调用梯度下降算法
iter_num,alpha,lambd=600000,0.001,1
w,cost_lst=grandient_reg(X,w,y,iter_num,alpha,lambd)
#绘制误差曲线
plt.plot(range(iter_num),cost_lst,"b-o")
[<matplotlib.lines.Line2D at 0x1423279f070>]
#绘制线性的决策边界
x_exmal=np.linspace(np.min(X[:,1]),np.max(X[:,1]),50)
x2=(-w[0,0]-w[1,0]*x_exmal)/(w[2,0])
plt.plot(x_exmal,x2,"r-o")
plt.scatter(X[y==1,1],X[y==1,2],color="b",marker="o")
plt.scatter(X[y==0,1],X[y==0,2],color="c",marker="^")
plt.show()
y.shape,X.shape,w.shape
((200,), (200, 3), (3, 1))
#计算准确率
y_pred=[1 if item>=0.5 else 0 for item in sigmoid(X@w).ravel()]
y_pred=np.array(y_pred)
np.sum(y_pred==y)/y.shape[0]
0.97
实验4 完成3.3 调用SKLEARN完成分类
3.4 调用SKLEARN
from sklearn.linear_model import LogisticRegression
clf = LogisticRegression().fit(X, y)
clf.score(X,y)
0.97