原文作者:aircraft 原文链接:https://www.cnblogs.com/DOMLX/p/9751471.html 1.np.random.random()函数参数 np.random.random
实验1(较强的正相关关系): x1 = np.random.random(100) y1 = np.random.random(100) + x1 plt.scatter(x1,...实验2(几乎没有线性相关关系): x2 = np.random.random(100) y2 = np.random.random(100) plt.scatter(x2, y2...x3 = np.random.random(100) y3 = np.random.random(100) + x3 * 50 plt.scatter(x3, y3, marker='....(100) y1 = np.random.random(100) + x1 plt.scatter(x1, y1, marker='.')...(100) y2 = np.random.random(100) plt.scatter(x2, y2, marker='.')
以此类推 我们可以看到,exp就是求e的多少次方 而sqrt则表示根号,也就是进行开方运算 我们可以得到,0的开方为0,1 的开方为1,2的开方为1.4 看下面的代码: a = np.floor(10*np.random.random...a = np.floor(10*np.random.random((2,2))) b = np.floor(10*np.random.random((2,2))) print(a) print("***...可以看到,成功将列进行了拼接 a = np.floor(10*np.random.random((2,12))) print(a) print("*******") print(np.hsplit(a,...3)) print("*******") print(np.hsplit(a,(3,4))) a = np.floor(10*np.random.random((12,2))) print("*****
---- floor import numpy as np # 向下取整 a = np.floor(10 * np.random.random((3, 4))) print(a) print(a.shape...---- resize/shape import numpy as np # 向下取整 a = np.floor(10 * np.random.random((3, 4))) # 改变a的结构 a.shape...---- hstack import numpy as np a = np.floor(10 * np.random.random((3, 3, 5))) b = np.floor(10 * np.random.random...((3, 3, 5))) c = np.floor(10 * np.random.random((3, 5))) d = np.floor(10 * np.random.random((3, 5)))...---- hsplit import numpy as np a = np.floor(10 * np.random.random((2, 12))) print("a=", a) print("--
实例代码: import talib as tb import numpy as np inputs = { 'open': np.random.random(1), 'high':...np.random.random(1), 'low': np.random.random(1), 'close': np.random.random(1) } avgPrice=tb.AVGPRICE
28)个节点;3层hidden layer,每一层20个节点;output layer有10个节点,分别表示输出为0-9的概率 2.初始化所有w为(-1, 1)的随机值 self.w1 = 2 * np.random.random...((784, 20)) - 1 #limit to (-1, 1) self.w2 = 2 * np.random.random((20, 20)) - 1 self.w3 = 2 * np.random.random...((20, 20)) - 1 self.w4 = 2 * np.random.random((20, 10)) - 1 3.前向传播,计算每一层输入输出的关系 def forward_prop(self...((784, 20)) - 1 #limit to (-1, 1) self.w2 = 2 * np.random.random((20, 20)) - 1 self.w3...= 2 * np.random.random((20, 20)) - 1 self.w4 = 2 * np.random.random((20, 10)) - 1 self.error
((1000, 20)) y_train = np.random.randint(2, size=(1000, 1)) x_test = np.random.random((100, 20)) y_test...((1000, timesteps, data_dim)) y_train = np.random.random((1000, num_classes)) # 生成虚拟验证数据 x_val = np.random.random...((100, timesteps, data_dim)) y_val = np.random.random((100, num_classes)) model.fit(x_train, y_train...((batch_size * 10, timesteps, data_dim)) y_train = np.random.random((batch_size * 10, num_classes))...# 生成虚拟验证数据 x_val = np.random.random((batch_size * 3, timesteps, data_dim)) y_val = np.random.random((
_1 = np.zeros((100, 20)) self.w2 = 2 * np.random.random((20, 20)) - 1 self.z2 = 2 * np.random.random...((100, 20)) - 1 self.hidden_layer_2 = np.zeros((100, 20)) self.w3 = 2 * np.random.random...((100, 20)) self.w4 = 2 * np.random.random((20, 1)) - 1 self.z4 = 2 * np.random.random..._1 = np.zeros((100, 20)) self.w2 = 2 * np.random.random((20, 20)) - 1 self.z2 = 2 * np.random.random...((100, 20)) self.w4 = 2 * np.random.random((20, 1)) - 1 self.z4 = 2 * np.random.random
算法简单实现 创建训练数据集&待分类数据 # 导包 import numpy as np import matplotlib.pyplot as plt train_data = np.array([np.random.random...reshape(10,2) # 创建训练集 train_lable = np.array([0,1,1,1,0,0,1,1,0,1]) # 给每个向量一个标签 test_data = np.array([np.random.random...()*10,np.random.random()*10]) # 待分类的目标值 查看数据分布 ?...matplotlib.pyplot as plt from sklearn.neighbors import KNeighborsClassifier # 导包 train_data = np.array([np.random.random...()*10,np.random.random()*10]) # 待分类的目标值 knn_clf.predict(test_data) # 使用训练类型进行预测 手动模型性能评估 加载sklearn中鸢尾花的数据
1,1]]) #output dataset y = np.array([[0,1,1,0]]).T 1 #the first-hidden layer weight value syn0 = 2*np.random.random...((2,3)) - #the hidden-hidden layer weight value syn1 = 2*np.random.random((3,2)) - 1 #the hidden-output...layer weight value syn2 = 2*np.random.random((2,1)) - 1 for j in range(60000): #the first layer
1、使用for import numpy as np from datetime import datetime img=np.random.random([10000,10000]) start_time...datetime.now()-start_time) # 0:00:27.886609 2、使用list import numpy as np from datetime import datetime img=np.random.random...datetime.now()-start_time) # 0:00:13.985333 3、set import numpy as np from datetime import datetime img=np.random.random
python中的实现: # -*- coding:utf-8 -*- import numpy as np x=np.random.random(10) y=np.random.random(10)...(10) y=np.random.random(10) #方法一:根据公式求解 d1=np.sum(np.abs(x-y)) print('d1:',d1) #方法二:根据scipy库求解 from...python中的实现: # -*- coding: utf-8 -*- import numpy as np x=np.random.random(10) y=np.random.random(10)...python 中的实现: # -*- coding: utf-8 -*- import numpy as np x = np.random.random(10) y = np.random.random...在python中的实现: # -*- coding: utf-8 -*- import numpy as np x=np.random.random(10) y=np.random.random(10
cv2.absdiff() 计算两个矩阵差值的绝对值 matrix_a = np.random.random([5,5]) matrix_b = np.random.random([5,5])...res = cv2.absdiff(matrix_a, matrix_b) 2. cv2.add() 实现两个矩阵逐元素相加 matrix_a = np.random.random([5,5])...matrix_b = np.random.random([5,5]) res = cv2.add(matrix_a, matrix_b) 3. cv2.addWeighted() 实现两个矩阵逐元素加权求和...= src2i 代码示例 vector_1 = np.random.random([5,5,5,5]) vector_2 = np.random.random([5,5,5,5]) res = cv2...vector_1 = np.random.random([5,5]) res = cv2.completeSymm(vector_1, lowerToUpper=False) --> res array
numpy实现上述过求解的代码: import numpy as np import matplotlib.pyplot as plt ## 定义w和b feature_num = 10 w_real = np.random.random...(feature_num) b_real = np.random.random() ## 生成训练数据 instance_num = 1000 X = np.random.uniform(-100,100...,(feature_num, instance_num)) y = np.matmul(w_real, X) + b_real y = y + np.random.random(y.shape) ##...初始化参数 w = np.random.random(feature_num) b = np.random.random() iter_time = 20 step_size = 0.0001 loss_value
size=5) array([5, 3, 5, 1, 1]) np.random.random_integers(1,6,size=8) array([4, 2, 4, 2, 4, 3, 5, 6]) np.random.random...() 返回0-1之间指定维度下的随机数 np.random.random(size=None) np.random.random() 0.5446614807473444 np.random.random...size=(2,3)) array([[0.24849247, 0.24794785, 0.12318699], [0.38708798, 0.12982558, 0.67378513]]) np.random.random...0.84402436, 0.94049321, 0.44680034, 0.12482742]]]) np.random.seed() 设置随机种子,保证每次的结果相同 np.random.seed(20) np.random.random...() 0.5881308010772742 np.random.seed(20) np.random.random() 0.5881308010772742 np.random.seed(20) np.random.random
创建一个3x3x3的随机数组(★☆☆) (提示: np.random.random) Z = np.random.random((3, 3, 3)) print (Z) 13....创建一个10×10的随机数组,并找出该数组中的最大值与最小值(★☆☆) (提示: max, min) Z = np.random.random((10, 10)) Zmax, Zmin = Z.max(...创建一个长度为30的随机向量,并求它的平均值 (★☆☆) (提示: mean) Z = np.random.random(30) mean = Z.mean() print (mean) 15.
我们检测欧氏距离测量涉及的4个向量是否大于某个阈值: x1 = np.random.random(1000000) x2 = np.random.random(1000000) y1 = np.random.random...(1000000) y2 = np.random.random(1000000) %%timeit -n100 -r10 c = np.sqrt((x1-x2)**2+(y1-y2)**2) > 0.5...这里有一个例子: a = np.random.random(1000000) b = np.random.random(1000000) cplx = a + b*1j %%timeit -n100...(size=i) b = np.random.random(size=i) times = [0]*10 for j in range(10): t1 = time...(size=i) b = np.random.random(size=i) times = [0]*10 for j in range(10): t1 = time
全局最佳适应度值 # 迭代寻优 t = 0 record = np.zeros(maxgen) while t < maxgen: # 速度更新 v = w * v + c1 * np.random.random...() * (gbest - pop) + c2 * np.random.random() * (zbest.reshape(2, 1) - pop) v[v > Vmax] = Vmax #...pop[pop > popmax] = popmax # 限制位置 pop[pop < popmin] = popmin ''' # 自适应变异 p = np.random.random...如果这个数落在变异概率区间内,则进行变异处理 k = np.random.randint(0,2) # 在[0,2)之间随机选一个整数 pop[:,k] = np.random.random
import cv2 import numpy as np img3 = np.random.random((600, 800, 3)) while 1: img3 = np.random.random
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