raise ValueError, "Don't be so negative" return numpy.arange(1, n+1).cumprod() class FactorialTest(unittest.TestCase): def test_factorial(self): #对3的阶乘进行测试,应该能通过 self.assertEqual(6, factorial(3)[-1]) numpy.testing.assert_equal(numpy.array([1, 2, 6]), factorial(3)) ...
在平时的工作或学习中可能会碰到统计学中的假设检验问题，如常见的卡方检验、t检验以及正态性检验等，而这些检验的目的都是为了论证某个设想，并通过统计学的方法做解释。本期内容我们将跟大家分享几种常规的t检验的方法，以及这些方法的应用案例。
X_test = np.stack((x1, B1_1, B2_1, B3_1, B4_1, B5_1), axis = 1) predict_test = np.matmul(X_test,W) #预测数据输出 data = pd.DataFrame(predict_test) writer = pd.ExcelWriter(r"E:yynctryedata孟PP_模型预测.xlsx")# 写入Excel文件 data.to_excel(writer, 'page_1', float_fo...
import numpy as np i = np.eye(3) #eye(n)函数创建n维单位矩阵 print(i) np.savetxt('test.txt', i) #savetxt创建并保存test.txt文件 #读取csv文件 c,v=np.loadtxt('data.csv', delimiter=',', usecols=(6,7), unpack=True) """usecols 的参数为一个元组,以获取第7字段至第8字段的数据,也...
load('test.npy') print(ar_load) 3、存储文本文件 import os os.chdir('C:/Users/ypf/Desktop/') ar=np.random.rand(5,5) np.savetxt('test.txt',ar,delimiter=',') #delimiter指的是用什么分隔 print('finished') 注意默认存储为科学记数法,如果想存储浮点型: import os os.chdir('C:/Users/...
python -c "import numpy, sys; sys.exit(numpy.test() is False)" Code of Conduct NumPy is a community-driven open source project developed by a diverse group ofcontributors. The NumPy leadership has made a strong commitment to creating an open, inclusive, and positive community. Please read ...
A small demo showing how to compile Numba kernels for use on AWS Lambda - lambda-numba/dist/numpy/testing/nosetester.py at master · rlhotovy/lambda-numba
1. T检验 importpingouinaspg pg.ttest(x, y) Tdoftailp-valCI95%cohen-dBF10power T-test-4.59762858two-sided0.000024[-1.47, -0.58]1.187102786.3460.994771 2. 皮尔森相关 pg.corr(x, y) nrCI95%r2adj_r2p-valBF10power pearson300.60149[0.31, 0.79]0.361790.3145150.00043982.1160.955747 ...
arrs = np.load('test1.npz') # 查看数组名 print(arrs.files) # 获取数组 print(arrs['arr_0']) # 保存为文本文件 # np.loadtxt(FILENAME, dtype=int, delimiter=' ') # np.savetxt(FILENAME, a, fmt="%d", delimiter=",") np.savetxt('test',arr,fmt='%d', delimiter=',') ...
X_raw_test = X_raw[:,num_train_datum:] y_raw_train = y_raw[:,0:num_train_datum] y_raw_test = y_raw[:,num_train_datum:] 4. 数据标准化 训练集中的数据已进行了标准化处理,所以各标准化特征都呈零均值、单位方差的分布。然后可以将上述过程产生的定标器应用于测试集。 class scaler: def...