我的代码linreg.predict（）没有给出正确答案是什么问题？

Question

SO The question given to me was

Write a function that fits a polynomial LinearRegression model on the training data X_train for degrees 1, 3, 6, and 9. (Use PolynomialFeatures in sklearn.preprocessing to create the polynomial features and then fit a linear regression model) For each model, find 100 predicted values over the interval x = 0 to 10 (eg np.linspace(0,10,100)) and store this in a numpy array. The first row of this array should correspond to the output from the model trained on degree 1, the second row degree 3, the third row degree 6, and the fourth row degree 9.

So tried the problem myself and failed and saw some other persons GitHub code and was very similar to me but it worked.

So what is the difference between my code and the other person code?

Here is some basic code prior to my question

np.random.seed(0)
n = 15
x = np.linspace(0,10,n) + np.random.randn(n)/5
y = np.sin(x)+x/6 + np.random.randn(n)/10


X_train, X_test, y_train, y_test = train_test_split(x, y, random_state=0)

Here is my approach

pred=np.linspace(0,10,100).reshape(100,1)
k=np.zeros((4,100))

for count,i in enumerate([1,3,6,9]):   
    poly = PolynomialFeatures(degree=i)
    X_poly = poly.fit_transform(X_train.reshape(-1,1))
    linreg = LinearRegression()
    linreg.fit(X_poly,y_train.reshape(-1,1))
    pred = poly.fit_transform(pred.reshape(-1,1))
    t=linreg.predict(pred)
    #print(t)                      #used for debugging
    print("###   ****   ####")     #used for debugging
    k[count,:]=t.reshape(1,-1)    


print(k)

Here is the code that works

result = np.zeros((4, 100))
for i, degree in enumerate([1, 3, 6, 9]):
    poly = PolynomialFeatures(degree=degree)
    X_poly = poly.fit_transform(X_train.reshape(11,1))
    linreg = LinearRegression().fit(X_poly, y_train)
    y=linreg.predict(poly.fit_transform(np.linspace(0,10,100).reshape(100,1)))
    result[i, :] = y
print(result)

My approach got an error

     13     print("###   ****   ####")  
---> 14     k[count,:]=t.reshape(1,-1)
     15 
     16 

ValueError: could not broadcast input array from shape (200) into shape (100)

While other code worked fine

Answer 1

The difference lies in the argument for linreg.predict . You are overwriting your pred variable with the result of poly.fit_transform , which changes it's shape from (100,1) to (200,2) in the first iteration of the loop. In the second iteration, t does not fit into k anymore, resulting in the error you are facing.