importmatplotlib.pyplot as plt deffix_seed(seed=1):#重复观看一样东西 # reproducible np.random.seed(seed) # make up data建立数据 fix_seed(1) x_data=np.linspace(-7,10,2500)[:, np.newaxis]#水平轴-7~10 np.random.shuffle(x_data) noise=np.random.normal(0,8, x_data.shape) y_data=...
importmatplotlib.pyplotasplt deffix_seed(seed=1):#重复观看一样东西 # reproducible np.random.seed(seed) # make up data建立数据 fix_seed(1) x_data=np.linspace(-7,10,2500)[:,np.newaxis]#水平轴-7~10 np.random.shuffle(x_data) noise=np.random.normal(0,8,x_data.shape) y_data=np.squ...
import matplotlib.pyplot as plt def fix_seed(seed=1): #重复观看一样东西 # reproducible np.random.seed(seed) # make up data建立数据 fix_seed(1) x_data = np.linspace(-7, 10, 2500)[:, np.newaxis] #水平轴-7~10 np.random.shuffle(x_data) noise = np.random.normal(0, 8, x_data....
Numpy:利用Numpy库建立可视化输入的二次函数数据点集np.linspace+np.random.shuffle+np.random.normal 目录 输出结果 代码设计 输出结果 代码设计 import numpy as np import matplotlib.pyplot as plt def fix_seed(seed=1): #重复观看一样东西 # reproducible np.random.seed(seed) # make up data建立数据...
np.random.randint 生成指定数值范围内的随机整数 np.random.seed 按照种子来生成随机数,种子一样,则生成的结果必一致 np.random.shuffle 打乱数组元素顺序 np.random.uniform 均匀分布 np.random.choice 按照指定概率从指定集合中,生成随机数 选出几个给大家介绍一下 ...
If you are submitting a bug report, please fill in the following details and use the tag [bug]. Describe the bug Python code error Code example in environment.py, line 165 self.np_random, seed1 = seeding.np_random(seed) by looking to the...
import numpy as np import matplotlib.pyplot as plt def fix_seed(seed=1): #重复观看一样东西 # reproducible np.random.seed(seed) # make up data建立数据 fix_seed(1) x_data = np.linspace(-7, 10, 2500)[:, np.newaxis] #水平轴-7~10 np.random.shuffle(x_data) noise = np.random.norm...
np.random.shuffle(index) x_train = x_train[index,:] y_train = y_train[index]return[x_train, y_train]# fix random seed 开发者ID:iyytdeed,项目名称:Automatic-Modulation-Classification,代码行数:23,代码来源:train_LSTM_memLess.py 示例11: get_data_generator ...
np.random.choice does not seem to be following the np.random.seed the same way other np.random functions are, from within numba: Example code: choice_array = np.random.randint(300, size=1000).astype(np.int32) # select two random values w...
np.random.seed(20201123) x = np.random.uniform(0, 1, [3, 5]) np.save(outfile, x) y = np.load(outfile) print(y) # [[0.03911501 0.91357784 0.21820335 0.61869406 0.25371066] # [0.75731372 0.16270282 0.77498589 0.41520052 0.15138986]