statistics

问题 I am studying this snippet of python code. What does X = X[:, 1] mean in last line? def linreg(X,Y): # Running the linear regression X = sm.add_constant(X) model = regression.linear_model.OLS(Y, X).fit() a = model.params[0] b = model.params[1] X = X[:, 1] 回答1: x = np.random.rand(3,2) x Out[37]: array([[ 0.03196827, 0.50048646], [ 0.85928802, 0.50081615], [ 0.11140678, 0.88828011]]) x = x[:,1] x Out[39]: array([ 0.50048646, 0.50081615, 0.88828011]) So what that line did is sliced the array,

问题 Closed . This question needs details or clarity. It is not currently accepting answers. Want to improve this question? Add details and clarify the problem by editing this post. Closed 7 days ago . Improve this question Is there a fast way in R to find fisher's information and cramer rao lower bound? I have this data - > dput(my_vec) c(7.16523478153752, 5.66659652818595, 4.47575534893755, 4.84970857977856, 15.2276296414708, -0.573093658844655, 4.97980673868322, 2.73969325233614, 5

问题 Closed . This question needs details or clarity. It is not currently accepting answers. Want to improve this question? Add details and clarify the problem by editing this post. Closed 7 days ago . Improve this question Is there a fast way in R to find fisher's information and cramer rao lower bound? I have this data - > dput(my_vec) c(7.16523478153752, 5.66659652818595, 4.47575534893755, 4.84970857977856, 15.2276296414708, -0.573093658844655, 4.97980673868322, 2.73969325233614, 5

问题 In Java, I usually rely on the org.apache.commons.math3.random.EmpiricalDistribution class to do the following: Derive a probability distribution from observed data. Generate random values from this distribution. Is there any Python library that provides the same functionality? It seems like scipy.stats.gaussian_kde.resample does something similar, but I'm not sure if it implements the same procedure as the Java type I'm familiar with. 回答1: import numpy as np import scipy.stats import

问题 The formula below is a special case of the Wasserstein distance/optimal transport when the source and target distributions, x and y (also called marginal distributions) are 1D, that is, are vectors. where F^{-1} are inverse probability distribution functions of the cumulative distributions of the marginals u and v , derived from real data called x and y , both generated from the normal distribution: import numpy as np from numpy.random import randn import scipy.stats as ss n = 100 x = randn(n

问题 The formula below is a special case of the Wasserstein distance/optimal transport when the source and target distributions, x and y (also called marginal distributions) are 1D, that is, are vectors. where F^{-1} are inverse probability distribution functions of the cumulative distributions of the marginals u and v , derived from real data called x and y , both generated from the normal distribution: import numpy as np from numpy.random import randn import scipy.stats as ss n = 100 x = randn(n

问题 The formula below is a special case of the Wasserstein distance/optimal transport when the source and target distributions, x and y (also called marginal distributions) are 1D, that is, are vectors. where F^{-1} are inverse probability distribution functions of the cumulative distributions of the marginals u and v , derived from real data called x and y , both generated from the normal distribution: import numpy as np from numpy.random import randn import scipy.stats as ss n = 100 x = randn(n

问题 I am trying to make a table of all my numerical variables (i.e. feature) in the following format: Feature | Count | % Missing | Cardinality | Min. | 1st Quartile | Mean | Median | 3rd Quartile | Max. | Std. Dev. | --------|-------|-----------|-------------|------|--------------|------|--------|--------------|------|-----------| | | | | | | | | | | | So each row signifies a specific numeric variable and each column the statistics shown above (Count, % Missing, Cardinality, Min., 1st Quartile,

问题 I am trying to make a table of all my numerical variables (i.e. feature) in the following format: Feature | Count | % Missing | Cardinality | Min. | 1st Quartile | Mean | Median | 3rd Quartile | Max. | Std. Dev. | --------|-------|-----------|-------------|------|--------------|------|--------|--------------|------|-----------| | | | | | | | | | | | So each row signifies a specific numeric variable and each column the statistics shown above (Count, % Missing, Cardinality, Min., 1st Quartile,

问题 I am trying to make a table of all my numerical variables (i.e. feature) in the following format: Feature | Count | % Missing | Cardinality | Min. | 1st Quartile | Mean | Median | 3rd Quartile | Max. | Std. Dev. | --------|-------|-----------|-------------|------|--------------|------|--------|--------------|------|-----------| | | | | | | | | | | | So each row signifies a specific numeric variable and each column the statistics shown above (Count, % Missing, Cardinality, Min., 1st Quartile,