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ndm13002 committed Apr 13, 2017
1 parent a4f6ef5 commit e15de78
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Showing 7 changed files with 131 additions and 0 deletions.
50 changes: 50 additions & 0 deletions boussinesq_lookup.m
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function [sigma_z]=boussinesq_lookup(q,a,b,z)
fmn= [0.1,0.02926,0.03007,0.03058
0.2,0.05733,0.05894,0.05994
0.3,0.08323,0.08561,0.08709
0.4,0.10631,0.10941,0.11135
0.5,0.12626,0.13003,0.13241
0.6,0.14309,0.14749,0.15027
0.7,0.15703,0.16199,0.16515
0.8,0.16843,0.17389,0.17739];

m = a/z;
n = b/z;

if (n>1.5)
n=1.6;
elseif (1.3<=n)
if (n<=1.5)
n=1.4;
end
elseif 1.3<=n
n=1.2;
end

c = ones(1,4);
d = ones(1,4);

for i=1:4
[~,p]=min(abs(m-fmn(:,1)));
M =fmn(p,1);
fmn(p,1)=0;
if n==1.2
t=fmn(p,2);
elseif n==1.4
t=fmn(p,3);
elseif n==1.6
t=fmn(p,4);
end

d(i) = M;
c(i) = t;
end

b1=c(1);
b2=(c(2)-c(1))/(d(2)-d(1));
b3=(((c(3)-c(2))/(d(3)-d(2)))-((c(2)-c(1))/(d(2)-d(1)))/(d(3)-d(1)));
b4=(((c(4)-c(3))/(d(4)-d(3)))-((c(3)-c(2))/(d(3)-d(2)))-((c(2)-c(1))/(d(2)-d(1))))/(d(4)-d(1));
f3=b1+(b2*(m-d(1)))+(b3*(m-d(1))*(m-d(2)))+(b4*(m-d(1))*(m-d(2))*(m-d(3)));
sigma_z=q*f3;

end
21 changes: 21 additions & 0 deletions cost_logistic.m
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function [cost, gradient] = cost_logistic(a, x, y)
cost = 0;
gradient = 0;
t = a(1)+a(2).*x;
sigma = 1./(1+exp(-t));
cost = sum(-y.*log(sigma)- (1-y).*log(1-sigma));
costFun = @ (a) sum(-y.*log((1./(1+exp(-(a(1)+a(2).*x)))))-(1-y).*log(1-(1./(1+exp(-(a(1)+a(2).*x))))));
gradient = (1/length(x))*sum((sigma-y).*t);
ai = [0 0];
% Set options for fminunc
options = optimset('GradObj','on','MaxIter',400);
% Run fminunc to obtain the optimal theta
% This function will return theta and the cost
[theta, cost] = fminunc(costFun, ai);
t = theta(1)+theta(2).*x;
sigma = 1./(1+exp(-t));
plot(x,y,'xb', x, sigma);
title('Regression')
xlabel('Temp (Degrees F)')
ylabel('Pass or Fail (1 or 0)')
end
26 changes: 26 additions & 0 deletions initializeData.m
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c = [1.0000 53.0000 1.0000;
2.0000 57.0000 1.0000;
3.0000 58.0000 1.0000;
4.0000 63.0000 1.0000;
5.0000 66.0000 0;
6.0000 66.8000 0;
7.0000 67.0000 0;
8.0000 67.2000 0;
9.0000 68.0000 0;
10.0000 69.0000 0;
11.0000 69.8000 1.0000;
12.0000 69.8000 0;
13.0000 70.2000 1.0000;
14.0000 70.2000 0;
15.0000 72.0000 0;
16.0000 73.0000 0;
17.0000 75.0000 0;
18.0000 75.0000 1.0000;
19.0000 75.8000 0;
20.0000 76.2000 0;
21.0000 78.0000 0;
22.0000 79.0000 0;
23.0000 81.0000 0];
a = c(:,1);
x = c(:,2);
y = c(:,3);
8 changes: 8 additions & 0 deletions least_squares.m
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function [a,fx,r2] = least_squares(Z,y)
a = Z\y;
Sr = sum((y-Z*a).^2);
r2 = 1-Sr/sum((y-mean(y)).^2);
x = Z(:,2);
fx = a(1)+(a(2)*x)+(a(3)*x.^2);
end

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23 changes: 23 additions & 0 deletions problem_1_data.m
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clear
clc

% part a
xa=[1 2 3 4 5]';
ya=[2.2 2.8 3.6 4.5 5.5]';

Za = [ones(size(xa)), xa, xa.^-1];
[a1,fx1,r2_1] = least_squares(Za,ya);

% part b
xb=[3 4 5 7 8 9 11 12]';
yb=[1.6 3.6 4.4 3.4 2.2 2.8 3.8 4.6]';

Zb = [ones(size(xb)), xb, xb.^2, xb.^3];
[a2,fx2,r2_2] = least_squares(Zb,yb);

% part c
xc=[0.5 1 2 3 4 5 6 7 9]';
yc=[6 4.4 3.2 2.7 2.2 1.9 1.7 1.4 1.1]';

Zc = [exp(-1.5*xc),exp(-0.3*xc), exp(-0.05*xc)];
[a3,fx3,r2_3] = least_squares(Zc,yc);
3 changes: 3 additions & 0 deletions setdefaults.m
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set(0, 'defaultAxesFontSize', 16)
set(0,'defaultTextFontSize',14)
set(0,'defaultLineLineWidth',3)

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