random.rand() print(a,type(a)) # 生成一个随机浮点数 b = np.random.rand(4) print(b,type(b)) # 生成形状为4的一维数组 c = np.random.rand(2,3) print(c,type(c)) # 生成形状为2*3的二维数组,注意这里不是((2,3)) samples1 = np.random.rand(1000) samples2 = np.random.rand(100...
1、矩阵生成 要使用numpy需要先将numpy导入,代码为import numpy as np,接着就可以用np来进行操作了。data = np.random.randn(2,3)就可以生成一个2x3的随机数字矩阵。如果要生成矩阵就用data2 = np.array([1,2,3], [4,5,6])。使用data.shap来查看矩阵的行列数,data.dtype来查看矩阵里面的数据类型。
1、矩阵生成 要使用numpy需要先将numpy导入,代码为import numpy as np,接着就可以用np来进行操作了。data = np.random.randn(2,3)就可以生成一个2x3的随机数字矩阵。如果要生成矩阵就用data2 = np.array([1,2,3], [4,5,6])。使用data.shap来查看矩阵的行列数,data.dtype来查看矩阵里面的数据类型。
np.logspace(2,4,2)#array([ 100., 10000.]) 等比数组 np.zeros(2,3)#2行3列全为0的数组 np.eye(3)#标准数组 np.ones((1,3))#一行三列全为1的数组 np.bool/int/float64(12)#类型转换 np.random.random((2,3))#随机生成2行3列 np.random.rand(2,3)#随机生成2行3列正态分布的数组 包...
In[1]: %matplotlib inline import numpy as np import matplotlib.pyplot as plt plt.style.use('seaborn-white') data = np.random.randn(1000) In[2]: plt.hist(data); Figure 4-35. A simple histogram The hist() function has many options to tune both the calculation and the display; here’...
Y= np.random.normal(loc=0.0, scale=1.0, size=10000) X= np.random.random(size=10000) lower_right.scatter(X, Y) top_histogram.hist(X, bins=100) s= side_histogram.hist(Y, bins=100, orientation='horizontal')## clear the histograms and plot normed histogramstop_histogram.clear() ...
= str(np.random.randint(10000, 20000)) main() Output RuntimeError: Input tensor data type is not supported for NCCL process group: ComplexFloat NCCL backend should support the Complex datatype as in the backpropagation algorithm. Versions ...
import paddle import pickle as pk import numpy as np import matplotlib.pyplot as plt from model import LatentModulatedSiren # 这个就是我们的模型F_theta from utils import get_coordinate_grid %matplotlib inline /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/__init_...
r = a*np.sin(4*t) + np.random.randn(N)*0.2 # radius X[ix] = np.c_[r*np.sin(t), r*np.cos(t)] Y[ix] = j X = X.T Y = Y.T return X, Y 读入数据 X,y = load_planar_dataset() 数据是人工造的一些数据(二分类),以下画出数据的分布 ...
pre = np.array([10.,1.,1.,1.,1.,1.,5.,1.]) def rprop(beta): return beta + 0.02*pre*np.random.randn(p) out = mcmc(init, mhKernel(lpost, rprop), thin=1000) See the full runnable script for further details. JAX The above R and Python scripts are fine, but both language...