[:train_size],data[train_size:]# 创建数据集函数defcreate_dataset(dataset,look_back=1):X,Y=[],[]foriinrange(len(dataset)-look_back-1):a=dataset[i:(i+look_back),0]X.append(a)Y.append(dataset[i+look_back,0])returnnp.
AI代码解释 classResize(object):def__init__(self,output_size):self.output_size=output_size def__call__(self,X,Y):_X=cv2.resize(X,self.output_size)w,h=self.output_size c=Y.shape[-1]_Y=np.zeros((h,w,c))foriinrange(Y.shape[-1]):_Y[...,i]=cv2.resize(Y[...,i],self....
N 数据库不支持SCROLL CURSOR。 arraysize Y - itersize Y - rowcount Y - rownumber Y - lastrowid Y - query Y - statusmessage Y - cast(oid,s) Y - tzinfo_factory Y - nextset() Y - setoutputsize(size[,column]) Y - COPY-related methods ...
1. 创建图 2. 节点 3. 边 4. 查看图的元素 5. 删除图的元素 6. 使用图构造函数 7. 访问边和...
import numpy as np # Create a new array from which we will select elements a = np.array([[1,2,3], [4,5,6], [7,8,9], [10, 11, 12]]) print(a) # Create an array of indices b = np.array([0, 2, 0, 1]) # Select one element from each row of a using the indices ...
import matplotlib.pyplot as plt# 创建数据size_of_groups = [12, 11, 3, 30]# 生成饼图plt.pie(size_of_groups)plt.show() 使用Matplotlib进行绘制,结果如下。22.树图 树图主要用来可视化树形数据结构,是一种特殊的层次类型,具有唯一的根节点,左子树,和右子树。
X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]]) # create an array y = np.array([1, 2, 3, 4]) # Create another array kf = KFold(n_splits=2) # Define the split - into 2 folds kf.get_n_splits(X) # returns the number of splitting iterations in the cross-val...
on the Numeric code base and adds features introduced by numarray as well as an extended C-API and the ability to create arrays of arbitrary type which also makes NumPy suitable for interfacing with general-purpose data-base applications. ...
array([[4, 0, 0], [0, 5, 0], [0, 0, 6]]) Create an array using repeating list (or seenp.tile) np.array([1, 2, 3] * 3) Output: array([1, 2, 3, 1, 2, 3, 1, 2, 3]) Repeat elements of an array usingrepeat. ...
c = torch.tensor(3.14159) # Create a scalar (zero-dimensional tensor) d = torch.tensor([]) # Create an empty tensor (of size (0,)) print(a) print(b) print(c) print(d) """ tensor([[ 0.1000, 1.2000], [ 2.2000, 3.1000], ...