AI代码解释 classCrop(object):def__init__(self,min_size_ratio,max_size_ratio=(1,1)):self.min_size_ratio=np.array(list(min_size_ratio))self.max_size_ratio=np.array(list(max_size_ratio))def__call__(self,X,Y):size=np.array(X.shape[:2])mini=self.
from PIL import Image # open the original image original_img = Image.open("parrot1.jpg") #rotate image rot_180 = original_img.rotate(180, Image.NEAREST, expand = 1) # close all our files object I = np.array(original_img) I_rot = np.array(rot_180) original_img.close() I_grey ...
向表二中导入numpy数组 importnumpyasnpobj=np.array([[1,2,3],[4,5,6]])obj 输出：array([[1...
We can use the same function to generate multiple realizations or an array of random numbers from the same distribution. 我们可以使用同一个函数从同一个分布生成多个实现或一个随机数数组。 If I wanted to generate a 1d array of numbers,I will simply insert the size of that array, say 5 in ...
However, if you need to create a linear space with a half-open interval, [start, stop), then you can set the optional Boolean parameter endpoint to False:Python >>> np.linspace(-5, 5, 20, endpoint=False) array([-5. , -4.5, -4. , -3.5, -3. , -2.5, -2. , -1.5, -1....
conda_create(“r-reticulate”)第二步：在conda环境下安装“r-reticulate”和“numpy”；conda_install(“r-reticulate”,“numpy”)如果“numpy”已经安装，您不必再次安装这个包。上面的代码只是给个例子而已。第三步：加载包。numpy <- import(“numpy”)使用numpy数组 首先建立一个简单的numpy数组 y <- array...
importpulpimportnumpyasnp# Coefficients for the linear programming problemcoefficients = [1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-20,0,0,0,0,0,0,0,0,0,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-20,0,0,0,0,0,0,0,0,0,0,0,0...
List/Array Methods Dictionary Methods Tuple Methods Set Methods File Methods Python Keywords Python Exceptions Python Glossary Random Module Requests Module Math Module CMath Module Download Python Download Python from the official Python web site:https://python.org ...
bytearray #把byte变成 bytearray, 可修改的数组 8. bytes # bytes(“中国”,”gbk”) 9. callable # 判断⼀个对象是否可调⽤ 10. chr # 返回⼀个数字对应的ascii字符 , ⽐如chr(90)返回ascii⾥的’Z’ 11. classmethod #⾯向对象时⽤,现在忽略 12. compile #py解释器⾃⼰⽤的东⻄,...
(num): x = np.random.uniform(-10.0, 10.0) noise = np.random.normal(0, 1) y = w * x ** 2 + b * x + c + noise yield np.array([x]).astype(np.float32), np.array([y]).astype(np.float32) def create_dataset(num_data, batch_size=16, repeat_size=1): #数据增强函数 ...