Python 如何从随机生成的值中得到多项式的四阶，并将数组从（100）重塑为（100,4）？

我尝试实现多项式的四阶，并使用以下指令实现以下逻辑：

首先，从scikit学习中导入多项式特征函数

并使用它生成一个新的X_4d阵列，该阵列具有最多四阶的所有功能

变换后的形状应为（100,4），以添加高阶多项式特征，且前5个

示例应该如下所示

代码：

import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures

def forth_order(m, a=-3.15, b=1.18, c=3.52, d=3.92, mu=0.0, sigma=0.33):
    new_x = np.random.uniform(low=-1.0, high=1.0, size=(m,)).reshape(100,4)
    new_y =  a*x**3 + b*x**2 + c*x + d + np.random.randn(m)*sigma
    return(new_x, new_y)

print(new_x)
print(new_x.shape)

print(new_y)    
print(new_y.shape)

# to find the 4th order for random generated values
np.random.seed(42)
# call your function to generate the artificial cubic data set
new_x,new_y = make_cubic_dataset(100)

transformer = PolynomialFeatures(degree=4, include_bias=False)
transformer.fit(new_x).reshape(100,4)
x_ = transformer.transform(new_x)
X_4d = np.polyfit(x, y, 3) # fit a degree 4 (cubic) polynomial to the data

print(X_4d)
print(X_4d.shape)

ValueError: cannot reshape array of size 100 into shape (100,4)

import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures

def forth_order(m, a=-3.15, b=1.18, c=3.52, d=3.92, mu=0.0, sigma=0.33):
    new_x = np.random.uniform(low=-1.0, high=1.0, size=(m,)).reshape(-1,1)
    new_y =  a*x**3 + b*x**2 + c*x + d + np.random.randn(m)*sigma
    return(new_x, new_y)

np.random.seed(42)

# call your function to generate the artificial cubic data set for forth order
new_x,new_y = forth_order(100)

X_4d = transformer.transform(new_x)
X_4d = PolynomialFeatures(degree=4, include_bias=False).fit_transform(new_x)
transformer.fit(new_x)

print(X_4d.shape)
print(X_4d[:5,:])

错误：

import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures

def forth_order(m, a=-3.15, b=1.18, c=3.52, d=3.92, mu=0.0, sigma=0.33):
    new_x = np.random.uniform(low=-1.0, high=1.0, size=(m,)).reshape(100,4)
    new_y =  a*x**3 + b*x**2 + c*x + d + np.random.randn(m)*sigma
    return(new_x, new_y)

print(new_x)
print(new_x.shape)

print(new_y)    
print(new_y.shape)

# to find the 4th order for random generated values
np.random.seed(42)
# call your function to generate the artificial cubic data set
new_x,new_y = make_cubic_dataset(100)

transformer = PolynomialFeatures(degree=4, include_bias=False)
transformer.fit(new_x).reshape(100,4)
x_ = transformer.transform(new_x)
X_4d = np.polyfit(x, y, 3) # fit a degree 4 (cubic) polynomial to the data

print(X_4d)
print(X_4d.shape)

ValueError: cannot reshape array of size 100 into shape (100,4)

import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures

def forth_order(m, a=-3.15, b=1.18, c=3.52, d=3.92, mu=0.0, sigma=0.33):
    new_x = np.random.uniform(low=-1.0, high=1.0, size=(m,)).reshape(-1,1)
    new_y =  a*x**3 + b*x**2 + c*x + d + np.random.randn(m)*sigma
    return(new_x, new_y)

np.random.seed(42)

# call your function to generate the artificial cubic data set for forth order
new_x,new_y = forth_order(100)

X_4d = transformer.transform(new_x)
X_4d = PolynomialFeatures(degree=4, include_bias=False).fit_transform(new_x)
transformer.fit(new_x)

print(X_4d.shape)
print(X_4d[:5,:])

预期结果：

变换后的形状应为（100,4），以添加高阶多项式特征，前5个和样本应如下所示

print(X_4d.shape)
>>> (100, 4)

print(X_4d[:5,:])
>>> [[-0.25091976  0.06296073 -0.01579809  0.00396405]
    [ 0.90142861  0.81257354  0.73247704  0.66027576]
    [ 0.46398788  0.21528476  0.09988952  0.04634753]
    [ 0.19731697  0.03893399  0.00768234  0.00151586]
    [-0.68796272  0.4732927  -0.32560773  0.22400598]]

---

我在解决这个问题上遇到了麻烦

这是代码中的一个小改动，不言自明：

import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures

def forth_order(m, a=-3.15, b=1.18, c=3.52, d=3.92, mu=0.0, sigma=0.33):
    new_x = np.random.uniform(low=-1.0, high=1.0, size=(m,)).reshape(100,4)
    new_y =  a*x**3 + b*x**2 + c*x + d + np.random.randn(m)*sigma
    return(new_x, new_y)

print(new_x)
print(new_x.shape)

print(new_y)    
print(new_y.shape)

# to find the 4th order for random generated values
np.random.seed(42)
# call your function to generate the artificial cubic data set
new_x,new_y = make_cubic_dataset(100)

transformer = PolynomialFeatures(degree=4, include_bias=False)
transformer.fit(new_x).reshape(100,4)
x_ = transformer.transform(new_x)
X_4d = np.polyfit(x, y, 3) # fit a degree 4 (cubic) polynomial to the data

print(X_4d)
print(X_4d.shape)

ValueError: cannot reshape array of size 100 into shape (100,4)

import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures

def forth_order(m, a=-3.15, b=1.18, c=3.52, d=3.92, mu=0.0, sigma=0.33):
    new_x = np.random.uniform(low=-1.0, high=1.0, size=(m,)).reshape(-1,1)
    new_y =  a*x**3 + b*x**2 + c*x + d + np.random.randn(m)*sigma
    return(new_x, new_y)

np.random.seed(42)

# call your function to generate the artificial cubic data set for forth order
new_x,new_y = forth_order(100)

X_4d = transformer.transform(new_x)
X_4d = PolynomialFeatures(degree=4, include_bias=False).fit_transform(new_x)
transformer.fit(new_x)

print(X_4d.shape)
print(X_4d[:5,:])

输出

print(X_4d.shape)
>>> (100, 4)

print(X_4d[:5,:])
>>> [[-0.25091976  0.06296073 -0.01579809  0.00396405]
    [ 0.90142861  0.81257354  0.73247704  0.66027576]
    [ 0.46398788  0.21528476  0.09988952  0.04634753]
    [ 0.19731697  0.03893399  0.00768234  0.00151586]
    [-0.68796272  0.4732927  -0.32560773  0.22400598]]

---

您不能将包含100个元素（100x1）的阵列重塑为包含400个元素（100x4）的矩阵。不要使用

np.random.uniform（大小=（100，））。重塑（100,4）

只需使用

np.random.uniform（大小=（100,4））

创建随机矩阵。@Aiven感谢您的帮助，但我能够找到它来转换结果。生成输出。如果你喜欢我的答案，请接受这个解决方案。