distribution

问题 I am graphing the number of occurrences that a value occurs in two different datasets. One plot (plot 1) graphs perfectly, the bars are right above the numbers on the x-axis. On the second plot (plot 2), there should be two bars, one above the 1 x-axis value and the other above the 2 x-axis value, but both bars are thick and squashed between 1 and 2on the x-axis. How do I get the second graph to look like the first graph? This is the code that I used in Jupyter notebook to generate both plots

问题 I am graphing the number of occurrences that a value occurs in two different datasets. One plot (plot 1) graphs perfectly, the bars are right above the numbers on the x-axis. On the second plot (plot 2), there should be two bars, one above the 1 x-axis value and the other above the 2 x-axis value, but both bars are thick and squashed between 1 and 2on the x-axis. How do I get the second graph to look like the first graph? This is the code that I used in Jupyter notebook to generate both plots

问题 I am trying to find an optimal distribution curve fit to my data consisting of y-axis = [0, 0, 0, 0, 0.24, 0.53, 0.49, 0.64, 0.54, 0.78, 0.59, 0.44, 0.34, 0.88, 0.2, 0.49, 0.39, 0.39, 0.29, 0.2, 0.05, 0.05, 0.25, 0.05, 0.1, 0.15, 0.1, 0.1, 0.1, 0, 0, 0, 0, 0] y-axis are probabilities of an event occurring in x-axis time bins: x-axis = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0, 25.0, 26.0, 27.0, 28.0,

问题 I would like to know about the various options for distributing a Java application. I know that you can Distribute the Source Code and let users compile it themselves, or provide make files, etc.. Package it into a JAR, and have self extracting archives and (I'm sure, myriad other ways) I'm hoping for some explanations about the most common options (and one's I haven't thought of) and in particular, do they require a user to have a JVM, or can it be bundled with one - personally I'm not too

问题 I would like to know about the various options for distributing a Java application. I know that you can Distribute the Source Code and let users compile it themselves, or provide make files, etc.. Package it into a JAR, and have self extracting archives and (I'm sure, myriad other ways) I'm hoping for some explanations about the most common options (and one's I haven't thought of) and in particular, do they require a user to have a JVM, or can it be bundled with one - personally I'm not too

问题 i have about 20,000 rows of data like this,, Id | value 1 30 2 3 3 22 .. n 27 I did statistics to my data,, the average value 33.85, median 30.99, min 2.8, max 206, 95% confidence interval 0.21.. So most values around 33, and there are some outliers (a little).. So it seems like a distribution with long tail. I am new to both distribution and python,, i tried class fitter https://pypi.org/project/fitter/ to try many distribution from Scipy package,, and loglaplace distribution showed the

问题 i have about 20,000 rows of data like this,, Id | value 1 30 2 3 3 22 .. n 27 I did statistics to my data,, the average value 33.85, median 30.99, min 2.8, max 206, 95% confidence interval 0.21.. So most values around 33, and there are some outliers (a little).. So it seems like a distribution with long tail. I am new to both distribution and python,, i tried class fitter https://pypi.org/project/fitter/ to try many distribution from Scipy package,, and loglaplace distribution showed the

问题 i have about 20,000 rows of data like this,, Id | value 1 30 2 3 3 22 .. n 27 I did statistics to my data,, the average value 33.85, median 30.99, min 2.8, max 206, 95% confidence interval 0.21.. So most values around 33, and there are some outliers (a little).. So it seems like a distribution with long tail. I am new to both distribution and python,, i tried class fitter https://pypi.org/project/fitter/ to try many distribution from Scipy package,, and loglaplace distribution showed the

问题 I have fitted some distributions for sample data with the following code: import numpy as np import pylab import matplotlib.pyplot as plt from scipy.stats import norm samp = norm.rvs(loc=0,scale=1,size=150) # (example) sample values. figprops = dict(figsize=(8., 7. / 1.618), dpi=128) adjustprops = dict(left=0.1, bottom=0.1, right=0.97, top=0.93, wspace=0.2, hspace=0.2) import pylab fig = pylab.figure(**figprops) fig.subplots_adjust(**adjustprops) ax = fig.add_subplot(1, 1, 1) ax.hist(samp

问题 I have a distplot and I would like to plot a mean line that goes from 0 to the y value of the mean frequency. I want to do this, but have the line stop at when the distplot does. Why isn't there a simple parameter that does this? It would be very useful. I have some code that gets me almost there: plt.plot([x.mean(),x.mean()], [0, *what here?*]) This code plots a line just as I'd like except for my desired y-value. What would the correct math be to get the y max to stop at the frequency of