scipy

吴恩达深度学习学习笔记——C1W2——神经网络基础——作业2——用神经网络的思路实现Logistic回归

由 谁说胖子不能爱 提交于 2021-01-24 14:30:06
可以明确的说，如果不自己一步步调试作业代码，很难看懂作业内容。 这里主要梳理一下作业的主要内容和思路，完整作业文件可参考: http://localhost:8888/tree/Andrew-Ng-Deep-Learning-notes/assignments/C1W2 作业完整截图，参考本文结尾：作业完整截图。 作业指导及目标 Logistic Regression with a Neural Network mindset（用神经网络的思路实现Logistic回归） Welcome to your first (required) programming assignment! You will build a logistic regression classifier to recognize cats. This assignment will step you through how to do this with a Neural Network mindset, and so will also hone your intuitions about deep learning. Instructions: Do not use loops (for/while) in your code, unless the instructions explicitly ask

What does the letter k mean in the documentation of solve_ivp function of Scipy?

由 ぃ、小莉子 提交于 2021-01-24 12:07:43
问题 Solve_ivp is an initial value problem solver function from Scipy. In a few words scipy.integrate.solve_ivp(fun, t_span, y0, method=’RK45’, t_eval=None, dense_output=False, events=None, vectorized=False, args=None, **options) Solve an initial value problem for a system of ODEs. This function numerically integrates a system of ordinary differential equations given an initial value. In the solve_ivp function documentation (Scipy reference guide 1.4.1 page 695) we have the following Parameters

What does the letter k mean in the documentation of solve_ivp function of Scipy?

由 心不动则不痛 提交于 2021-01-24 12:06:52
问题 Solve_ivp is an initial value problem solver function from Scipy. In a few words scipy.integrate.solve_ivp(fun, t_span, y0, method=’RK45’, t_eval=None, dense_output=False, events=None, vectorized=False, args=None, **options) Solve an initial value problem for a system of ODEs. This function numerically integrates a system of ordinary differential equations given an initial value. In the solve_ivp function documentation (Scipy reference guide 1.4.1 page 695) we have the following Parameters

What does the letter k mean in the documentation of solve_ivp function of Scipy?

由 我怕爱的太早我们不能终老 提交于 2021-01-24 12:05:42
问题 Solve_ivp is an initial value problem solver function from Scipy. In a few words scipy.integrate.solve_ivp(fun, t_span, y0, method=’RK45’, t_eval=None, dense_output=False, events=None, vectorized=False, args=None, **options) Solve an initial value problem for a system of ODEs. This function numerically integrates a system of ordinary differential equations given an initial value. In the solve_ivp function documentation (Scipy reference guide 1.4.1 page 695) we have the following Parameters

Consistent ColumnTransformer for intersecting lists of columns

由 ε祈祈猫儿з 提交于 2021-01-24 08:17:31
问题 I want to use sklearn.compose.ColumnTransformer consistently (not parallel, so, the second transformer should be executed only after the first) for intersecting lists of columns in this way: log_transformer = p.FunctionTransformer(lambda x: np.log(x)) df = pd.DataFrame({'a': [1,2, np.NaN, 4], 'b': [1,np.NaN, 3, 4], 'c': [1 ,2, 3, 4]}) compose.ColumnTransformer(n_jobs=1, transformers=[ ('num', impute.SimpleImputer() , ['a', 'b']), ('log', log_transformer, ['b', 'c']), ('scale', p

scipy p-value returns 0.0

由 ﹥＞﹥吖頭↗ 提交于 2021-01-21 18:27:41
问题 Using a 2 sample Kolmogorov Smirnov test, I am getting a p-value of 0.0. >>>scipy.stats.ks_2samp(dataset1, dataset2) (0.65296076312083573, 0.0) Looking at the histograms of the 2 datasets, I am quite confident they represent two different datasets. But, really, p = 0.0? That doesn't seem to make sense. Shouldn't it be a very small but positive number? I know the return value is of type numpy.float64. Does that have something to do with it? EDIT: data here: https://www.dropbox.com/s

scipy p-value returns 0.0

由 生来就可爱ヽ(ⅴ＜●) 提交于 2021-01-21 18:26:26
问题 Using a 2 sample Kolmogorov Smirnov test, I am getting a p-value of 0.0. >>>scipy.stats.ks_2samp(dataset1, dataset2) (0.65296076312083573, 0.0) Looking at the histograms of the 2 datasets, I am quite confident they represent two different datasets. But, really, p = 0.0? That doesn't seem to make sense. Shouldn't it be a very small but positive number? I know the return value is of type numpy.float64. Does that have something to do with it? EDIT: data here: https://www.dropbox.com/s

Estimate confidence intervals for parameters of distribution in python

由 旧城冷巷雨未停 提交于 2021-01-21 10:31:58
问题 Is there a built in function that will provide the confidence intervals for parameter estimates in a python package or is this something I will need to implement by hand? I am looking for something similar to matlabs gevfit http://www.mathworks.com/help/stats/gevfit.html. 回答1: Take a look at scipy and numpy in case you haven't already. If you have some familiarity with MATLAB, then the switch should be relatively easy. I've taken this quick snippet from this SO response: import numpy as np

Estimate confidence intervals for parameters of distribution in python

由 ぐ巨炮叔叔 提交于 2021-01-21 10:30:20
问题 Is there a built in function that will provide the confidence intervals for parameter estimates in a python package or is this something I will need to implement by hand? I am looking for something similar to matlabs gevfit http://www.mathworks.com/help/stats/gevfit.html. 回答1: Take a look at scipy and numpy in case you haven't already. If you have some familiarity with MATLAB, then the switch should be relatively easy. I've taken this quick snippet from this SO response: import numpy as np

python: plotting a histogram with a function line on top

由 一曲冷凌霜 提交于 2021-01-20 16:30:44
问题 I'm trying to do a little bit of distribution plotting and fitting in Python using SciPy for stats and matplotlib for the plotting. I'm having good luck with some things like creating a histogram: seed(2) alpha=5 loc=100 beta=22 data=ss.gamma.rvs(alpha,loc=loc,scale=beta,size=5000) myHist = hist(data, 100, normed=True) Brilliant! I can even take the same gamma parameters and plot the line function of the probability distribution function (after some googling): rv = ss.gamma(5,100,22) x = np
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