statistics

问题 I want to create a subsclass of scipy.stats.rv_discrete to add some additional methods. However, when I try to access the pmf() method of the subclass, an error is raised. Please see the following example: import numpy as np from scipy import stats class sub_rv_discrete(stats.rv_discrete): pass xk = np.arange(2) pk = (0.5, 0.5) instance_subclass = sub_rv_discrete(values=(xk, pk)) instance_subclass.pmf(xk) This results in: Traceback (most recent call last): File "<ipython-input-48-129655c38e6a

问题 I'm trying to compare which feature selection model is more eficiente for a specific domain. Nowadays the state of the art in this domain (GWAS) is regression-based algorithms (LR, LMM, SAIGE, etc), but I want to give a try with tree-based algorithms (I'm using LightGBM LGBMClassifier with boosting_type='gbdt' as the cross-validation selected for me as most efficient one). I managed to get something like: Regression based alg --------------------- Features P-Values f1 2.49746e-21 f2 5.63324e

问题 Using Python, how can I sample data from a multivariate log-normal distribution? For instance, for a multivariate normal, there are two options. Let's assume we have a 3 x 3 covariance matrix and a 3-dimensional mean vector mu. # Method 1 sample = np.random.multivariate_normal(mu, covariance) # Method 2 L = np.linalg.cholesky(covariance) sample = L.dot(np.random.randn(3)) + mu I found numpy's numpy.random.lognormal, but that only seems to work for univariate samples. I also noticed scipy's

问题 I have a pandas DataFrame that has the following values in a Series x = [2, 1, 76, 140, 286, 267, 60, 271, 5, 13, 9, 76, 77, 6, 2, 27, 22, 1, 12, 7, 19, 81, 11, 173, 13, 7, 16, 19, 23, 197, 167, 1] I was instructed to plot two histograms in a Jupyter notebook with Python 3.6. No sweat right? x.plot.hist(bins=8) plt.show() I chose 8 bins because that looked best to me. I have also been instructed to plot another histogram with the log of x. x.plot.hist(bins=8) plt.xscale('log') plt.show() This

问题 Closed. This question is off-topic. It is not currently accepting answers. Want to improve this question? Update the question so it's on-topic for Stack Overflow. Closed 6 years ago . I have a program that builds a histogram chart and I want to add a bell curve that represents the ideal curve based on the goal I got from engineering. here is a snapshot to give you an idea of what im working with. I formatted the seconds to represent hh:mm:ss. In this example: bins: 20 the goal: 672 seconds

问题 I would like to write a SPSS macro to perform three operations: generate a custom table, clean the output window, export table. As you know SPSS macro facility allows to use two types of loops: 'numeric' like ( !do !i = !x !to !y ) and 'list'/'for each' like ( !do !i !in (!1) ). My goal is to create a macro with a call as below: col v1 v2 / "Sheet A" "Sheet B". working this way (with a 'list' like loop): Get first variable name (v1) Put it in the ctables macro section Get first sheet name

问题 I would like to write a SPSS macro to perform three operations: generate a custom table, clean the output window, export table. As you know SPSS macro facility allows to use two types of loops: 'numeric' like ( !do !i = !x !to !y ) and 'list'/'for each' like ( !do !i !in (!1) ). My goal is to create a macro with a call as below: col v1 v2 / "Sheet A" "Sheet B". working this way (with a 'list' like loop): Get first variable name (v1) Put it in the ctables macro section Get first sheet name

问题 I'm trying to create this kind of "side by side" barplot with seaborn and pandas . this is how I create data frame: dfs = pd.DataFrame(data={'investors': ['first','second','third'], 'stocks': [23, 123, 54], 'bonds': [54, 67, 123], 'real estate': [45, 243, 23]}) And here is barplot code: sns.factorplot(x='investors', y='bonds', data=dfs, kind='bar') Can anyone please help? Thanks 回答1: Use melt on your dateframe then plot it with seaborn. dfs = pd.DataFrame(data={'investors': ['first','second',

问题 I would like to compute the Earth Mover Distance between two 2D arrays (these are not images). Right now I go through two libraries: scipy (https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.wasserstein_distance.html) and pyemd (https://pypi.org/project/pyemd/). #define a sampeling method def sampeling2D(n, mu1, std1, mu2, std2): #sample from N(0, 1) in the 2D hyperspace x = np.random.randn(n, 2) #scale N(0, 1) -> N(mu, std) x[:,0] = (x[:,0]*std1) + mu1 x[:,1] = (x[:,1]*std2) +

问题 I am trying to eliminate outliers in SQL Server 2008 by standard deviation. I would like only records that contain a value in a specific column within +/- 1 standard deviation of that column's mean. How can I accomplish this? 回答1: If you are assuming a bell curve distribution of events, then only 68% of values will be within 1 standard deviation away from the mean (95% are covered by 2 standard deviations). I would load a variable with the standard deviation of your range (derived using stdev