np.zeros((n - 1,))

This code creates a numpy array of zeros with n-1 elements. The shape of the array is (n-1, ). The dtype of the array is float64 by default.

For example, if n=5, then the code creates an array of shape (4,) with all elements set to 0.0:

import numpy as np

n = 5
arr = np.zeros((n - 1,))
print(arr)

Output:
[0. 0. 0. 0.]

相关问题

Y1定义为LABEL输出，Y1 = np.zeros((self.n - s, 1))， Y2是特征输出 ：Y2 = np.zeros((self.n - s, self.m - 1))

好的，按照您的要求，修改后的函数如下所示：

def _slice_multi(self):
    # self.n 是数据集总行数
    # self.m 是列
    s = self.ps * self.skip + self.Ck - 1 + self.h - 1
    X1 = np.zeros((self.n - s, self.w, self.m))  # 短期数据集
    X2 = np.zeros((self.n - s, self.ps * self.Ck, self.m - 1))  # 长期数据集
    Y1 = np.zeros((self.n - s, 1))  # LABEL输出
    Y2 = np.zeros((self.n - s, self.m - 1))  # 特征输出
    for i in range(s, self.n):
        t = i - self.h + 1
        X1[i - s] = self.raw[t - self.w:t].copy()
        idx = []
        for k in range(self.ps):
            # 执行self.ps次，每次间隔self.skip长，取self.Ck=6， total = self.ps * self.Ck
            idx = list(range(t - self.Ck - k * self.skip, t - k * self.skip)) + idx
        idx = np.array(idx, dtype=int)
        X2[i - s] = self.raw[idx, :-1].copy()  # 去掉最后一列
        Y1[i - s] = self.raw[i, -1].copy()  # 最后一列为LABEL
        Y2[i - s] = self.raw[i, :-1].copy()  # 去掉最后一列
    # 返回特征和标签
    return (X1, X2), (Y1, Y2)

修改后的函数输出了两个元组，每个元组包含了两个数组，分别对应两个输出。其中Y1为LABEL输出，维度为 (n-s, 1)，Y2为特征输出，维度为 (n-s, m-1)。

请把这段代码改成jupyter环境中适用的代码：import numpy as np import matplotlib.pyplot as plt def gauss_1d_c(): # Basis of 1D Gaussians, with varying centers, # all with same width n = 101 nu = 1.0 h2m = 0.5 # Calculate the centers for the Gaussians s = np.zeros(n) for i in range(n): s[i] = -25.0 + (i - 1) * 0.5 # Setup the Hamiltonian h = np.zeros((n, n)) o = np.zeros((n, n)) f = np.zeros((n, n)) for i in range(n): for j in range(n): ss = (s[i] - s[j])**2 o[i, j] = np.exp(-0.5 * nu * ss) t = np.exp(-0.5 * nu * ss) * nu * h2m * (1.0 - nu * ss) p = 0.5 * np.exp(-0.5 * nu * ss) * 0.25 * (1.0 / nu + (s[i] + s[j])**2) h[i, j] = t + p # Diagonalize f=np.dot(h,np.linalg.inv(o)) eigenvalues, eigenvectors = np.linalg.eig(f) print(eigenvalues) def paint_eigenvalues(): # 在图中绘制特征值 plt.plot(eigenvalues) # 设置图的标题和坐标轴标签 plt.title("Eigenvalues") plt.xlabel("Index") plt.ylabel("Value") # 显示图 plt.show() gauss_1d_c()

代码已经是适用于jupyter环境的，但如果要在jupyter中显示图像，需要添加以下代码：

%matplotlib inline

将其添加到代码的开头即可。完整的代码如下所示：

%matplotlib inline
import numpy as np 
import matplotlib.pyplot as plt  

def gauss_1d_c():   
    # Basis of 1D Gaussians, with varying centers,   
    # all with same width     
    n = 101     
    nu = 1.0     
    h2m = 0.5      
    
    # Calculate the centers for the Gaussians     
    s = np.zeros(n)     
    for i in range(n):         
        s[i] = -25.0 + (i - 1) * 0.5    
        
    # Setup the Hamiltonian         
    h = np.zeros((n, n))         
    o = np.zeros((n, n))         
    f = np.zeros((n, n))     
    
    for i in range(n):         
        for j in range(n):             
            ss = (s[i] - s[j])**2             
            o[i, j] = np.exp(-0.5 * nu * ss)             
            t = np.exp(-0.5 * nu * ss) * nu * h2m * (1.0 - nu * ss)             
            p = 0.5 * np.exp(-0.5 * nu * ss) * 0.25 * (1.0 / nu + (s[i] + s[j])**2)             
            h[i, j] = t + p    
    
    # Diagonalize     
    f=np.dot(h,np.linalg.inv(o))     
    eigenvalues, eigenvectors = np.linalg.eig(f)     
    print(eigenvalues)  
    
def paint_eigenvalues():     
    # 在图中绘制特征值     
    plt.plot(eigenvalues)    
    # 设置图的标题和坐标轴标签     
    plt.title("Eigenvalues")     
    plt.xlabel("Index")     
    plt.ylabel("Value")    
    # 显示图     
    plt.show()  

gauss_1d_c()
paint_eigenvalues()

注意，需要先运行gauss_1d_c()函数，才能在paint_eigenvalues()函数中使用eigenvalues变量。