Why does my graph get sent to the end of the html when I publish it?

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I'm using MATLAB R2021b and am trying to format my homework to output the graphs within the document, but when I try to plot something with a function inside it it throws all my graphs at the end of the publication rather than in line where I set it.
Here is my code
%% Module 1 MATLAB Exercise
%% Part 1
% Compute and plot the discrete distribution for a Binomially % distributed
% random variable n where the number of trials N=10 and p=0.7. The MATLAB
% function "nchoosek.m” is helpful.*
N = 10; %Number of Trials
p = 0.7; %Probability
q = 1-p;
k = (0:10); %Random variable from 0 to 10
bar(k,arrayfun(@(x) nchoosek(N, x)*p^x*q^(N-x), k));
snapnow;
%%
% The bar graph is centered around 7 successes and falls off on either
% side and though it is extremely small, there still is a very small
% possibility that there will be 0 successes.
%% Part 2
% In a for loop run M times, perform 10 Bernoulli trials sum to get
% the number of successes for each set of 10 trials. When done, use the
% MATLAB "hist.m" function to plot the tabulated sums with 11 bins
% specified to "hist.m” for binning the the numbers 0 to 10. You should
% normalize the histogram so that it sums to one, i.e., divide by M. Do
% this for M=10, 100, and 1000000, plot and compare.*
%%
% Histogram of 10 Bernoulli Trials
histogram(TenBernoulliTrials(10),11,'BinEdges',0:10,'Normalization','probability');
snapnow;
%%
% At just 10 trials, the total varies greatly, sometimes matching the shape
% of the graph generated in Part 1 pretty well, sometimes looking very
% different. This is because 10 is barely enough trials to look like the
% first graph. There are not enough data points for there to be anything
% that goes beyond barely resembling the graph from part 1. This is because
% there are not enough trials at this point for there to be a clear
% distinction between the different results.
%%
% Histogram of 100 Bernoulli Trials
histogram(TenBernoulliTrials(100),11,'BinEdges',0:10,'Normalization','probability');
snapnow;
%%
% At 100 trials, the shape starts to fit the graph from part 1 much better,
% though there are some minute differences that can be seen in certain
% sections of the histogram.The distribution can clearly be seen, but the
% actual numbers do not have the resolution of the bar graph from part 1.
% This is because there are enough trials at this point for there to be a
% clear distinction between the different results, but not enough to
% accurately define the probability.
%%
% Histogram of 1000000 Bernoulli Trials
histogram(TenBernoulliTrials(1000000),11,'BinEdges',0:10,'Normalization','probability');
snapnow;
%%
% At 1000000 trials, the shape fits nearly exactly with the bar graph
% generated from Part 1. The shape of the graph matches nearly perfectly
% and the magnitudes of the normalized bar graph match the probability
% distribution from Part 1. This is because there are enough trials at this
% point to have a small enough resolution to generate values that are
% comparable to the first part.
%% Bernoulli Trial Function
function SUM = TenBernoulliTrials(M)
SUM = zeros(M,1);
for x = 1:M
for N = 1:10
if rand < 0.7
SUM(x) = SUM(x) + 1;
end
end
end
end
%%
% This is the function used to generate the histographs. It uses a random
% number generator to simulate the 0.7 probability for a success and sums
% the number of successes M number of times which is the output of the
% function.
And here's the ending of what I get when I publish it
I've attached the pdf of the full document

Risposte (1)

Ive J
Ive J il 29 Gen 2022
I cannot reproduce this behavior.
  1 Commento
Jason Yoon
Jason Yoon il 29 Gen 2022
The pdf you attached replicates the behavior exactly, the two graphs are at the end rather then when they are called

Accedi per commentare.

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