Rrerku

I am running logistic regression on a small dataset which looks like this:

After implementing gradient descent and the cost function, I am getting a 100% accuracy in the prediction stage, However I want to be sure that everything is in order so I am trying to plot the decision boundary line which separates the two datasets.

Below I present plots showing the cost function and theta parameters. As can be seen, currently I am printing the decision boundary line incorrectly.

Extracting data

clear all; close all; clc;

alpha = 0.01;
num_iters = 1000;

%% Plotting data
x1 = linspace(0,3,50);
mqtrue = 5;
cqtrue = 30;
dat1 = mqtrue*x1+5*randn(1,50);

x2 = linspace(7,10,50);
dat2 = mqtrue*x2 + (cqtrue + 5*randn(1,50));

x = [x1 x2]'; % X

subplot(2,2,1);
dat = [dat1 dat2]'; % Y

scatter(x1, dat1); hold on;
scatter(x2, dat2, '*'); hold on;
classdata = (dat>40);

Computing Cost, Gradient and plotting

% Setup the data matrix appropriately, and add ones for the intercept term
[m, n] = size(x);

% Add intercept term to x and X_test
x = [ones(m, 1) x];

% Initialize fitting parameters
theta = zeros(n + 1, 1);
%initial_theta = [0.2; 0.2];

J_history = zeros(num_iters, 1); 

plot_x = [min(x(:,2))-2, max(x(:,2))+2]

for iter = 1:num_iters 
% Compute and display initial cost and gradient
 [cost, grad] = logistic_costFunction(theta, x, classdata);
 theta = theta - alpha * grad;
 J_history(iter) = cost;

 fprintf('Iteration #%d - Cost = %d... rn',iter, cost);


 subplot(2,2,2);
 hold on; grid on;
 plot(iter, J_history(iter), '.r'); title(sprintf('Plot of cost against number of iterations. Cost is %g',J_history(iter)));
 xlabel('Iterations')
 ylabel('MSE')
 drawnow

 subplot(2,2,3);
 grid on;
 plot3(theta(1), theta(2), J_history(iter),'o')
 title(sprintf('Tita0 = %g, Tita1=%g', theta(1), theta(2)))
 xlabel('Tita0')
 ylabel('Tita1')
 zlabel('Cost')
 hold on;
 drawnow

 subplot(2,2,1);
 grid on; 
 % Calculate the decision boundary line
 plot_y = theta(2).*plot_x + theta(1); % <--- Boundary line 
 % Plot, and adjust axes for better viewing
 plot(plot_x, plot_y)
 hold on;
 drawnow

end

fprintf('Cost at initial theta (zeros): %fn', cost);
fprintf('Gradient at initial theta (zeros): n');
fprintf(' %f n', grad);

The above code is implementing gradient descent correctly (I think) but I am still unable to show the boundary line plot. Any suggestions would be appreciated.

Regarding the code

You should plot the decision boundary after training is finished, not inside the training loop, parameters are constantly changing there; unless you are tracking the change of decision boundary.

Decision boundary

Assuming that input is $boldsymbolx=(x_1, x_2)$, and parameter is $boldsymboltheta=(theta_0, theta_1,theta_2)$, here is the line that should be drawn as decision boundary:
$$x_2 = -fractheta_1theta_2 x_1 - fractheta_0theta_2$$
which can be drawn in $(Bbb R^+, Bbb R^+)$ by connecting two points $(0, - fractheta_0theta_2)$ and $(- fractheta_0theta_1, 0)$.
However, if $theta_2=0$, the line would be $x_1=-fractheta_0theta_1$.

Where this comes from?

Decision boundary of Logistic regression is the set of all points $boldsymbolx$ that satisfy
$$Bbb P(y=1|boldsymbolx)=Bbb P(y=0|boldsymbolx) = frac12.$$
Given
$$Bbb P(y=1|boldsymbolx)=frac11+e^-boldsymboltheta^tboldsymbolx_+$$
where $boldsymboltheta=(theta_0, theta_1,cdots,theta_d)$, and $boldsymbolx$ is extended to $boldsymbolx_+=(1, x_1, cdots, x_d)$ for the sake of readability to have$$boldsymboltheta^tboldsymbolx_+=theta_0 + theta_1 x_1+cdots+theta_d x_d,$$
decision boundary can be derived as follows
$$beginalign*
&frac11+e^-boldsymboltheta^tboldsymbolx_+ = frac12 \
&Rightarrow boldsymboltheta^tboldsymbolx_+ = 0\
&Rightarrow theta_0 + theta_1 x_1+cdots+theta_d x_d = 0
endalign*$$
For two dimensional input $boldsymbolx=(x_1, x_2)$ we have
$$beginalign*
& theta_0 + theta_1 x_1+theta_2 x_2 = 0 \
& Rightarrow x_2 = -fractheta_1theta_2 x_1 - fractheta_0theta_2
endalign*$$
which is the separation line that should be drawn in $(x_1, x_2)$ plane.

Weighted decision boundary

If we want to weight the positive class ($y = 1$) more or less using $w$, here is the general decision boundary:
$$wBbb P(y=1|boldsymbolx) = Bbb P(y=0|boldsymbolx) = fracww+1$$

For example, $w=2$ means point $boldsymbolx$ will be assigned to positive class if $Bbb P(y=1|boldsymbolx) > 0.33$ (or equivalently if $Bbb P(y=0|boldsymbolx) < 0.66$), which implies favoring the positive class (increasing the true positive rate).

Here is the line for this general case:
$$beginalign*
&frac11+e^-boldsymboltheta^tboldsymbolx_+ = frac1w+1 \
&Rightarrow e^-boldsymboltheta^tboldsymbolx_+ = w\
&Rightarrow boldsymboltheta^tboldsymbolx_+ = -textlnw\
&Rightarrow theta_0 + theta_1 x_1+cdots+theta_d x_d = -textlnw
endalign*$$

Your decision boundary is a surface in 3D as your points are in 2D.

With Wolfram Language

Create the data sets.

mqtrue = 5;
cqtrue = 30;
With[x = Subdivide[0, 3, 50],
 dat1 = Transpose@x, mqtrue x + 5 RandomReal[1, Length@x];
 ];
With[x = Subdivide[7, 10, 50],
 dat2 = Transpose@x, mqtrue x + cqtrue + 5 RandomReal[1, Length@x];
 ];

View in 2D (ListPlot) and the 3D (ListPointPlot3D) regression space.

ListPlot[dat1, dat2, PlotMarkers -> "OpenMarkers", PlotTheme -> "Detailed"]

I Append the response variable to the data.

datPlot =
 ListPointPlot3D[
 MapThread[Append, #, Boole@Thread[#[[All, 2]] > 40]] & /@ dat1, dat2
 ]

Perform a Logistic regression (LogitModelFit). You could use GeneralizedLinearModelFit with ExponentialFamily set to "Binomial" as well.

With[dat = Join[dat1, dat2],
 model =
 LogitModelFit[
 MapThread[Append, dat, Boole@Thread[dat[[All, 2]] > 40]],
 x, y, x, y]
 ]

From the FittedModel "Properties" we need "Function".

model["Properties"]

AdjustedLikelihoodRatioIndex, DevianceTableDeviances, ParameterConfidenceIntervalTableEntries,
AIC, DevianceTableEntries, ParameterConfidenceRegion,
AnscombeResiduals, DevianceTableResidualDegreesOfFreedom, ParameterErrors,
BasisFunctions, DevianceTableResidualDeviances, ParameterPValues,
BestFit, EfronPseudoRSquared, ParameterTable,
BestFitParameters, EstimatedDispersion, ParameterTableEntries,
BIC, FitResiduals, ParameterZStatistics,
CookDistances, Function, PearsonChiSquare,
CorrelationMatrix, HatDiagonal, PearsonResiduals,
CovarianceMatrix, LikelihoodRatioIndex, PredictedResponse,
CoxSnellPseudoRSquared, LikelihoodRatioStatistic, Properties,
CraggUhlerPseudoRSquared, LikelihoodResiduals, ResidualDeviance,
Data, LinearPredictor, ResidualDegreesOfFreedom,
DesignMatrix, LogLikelihood, Response,
DevianceResiduals, NullDeviance, StandardizedDevianceResiduals,
Deviances, NullDegreesOfFreedom, StandardizedPearsonResiduals,
DevianceTable, ParameterConfidenceIntervals, WorkingResiduals,
DevianceTableDegreesOfFreedom, ParameterConfidenceIntervalTable

model["Function"]

Use this for prediction

model["Function"][8, 54]

0.0196842

and plot the decision boundary surface in 3D along with the data (datPlot) using Show and Plot3D

modelPlot =
 Show[
 datPlot,
 Plot3D[
 model["Function"][x, y],
 Evaluate[
 Sequence @@ 
 MapThread[Prepend, MinMax /@ Transpose@Join[dat1, dat2], x, y]],
 Mesh -> None,
 PlotStyle -> Opacity[.25, Green],
 PlotPoints -> 30
 ]
 ]

With ParametricPlot3D and Manipulate you can examine decision boundary curves for values of the variables. For example, keeping x fixed and letting y vary.

Manipulate[
 Show[
 modelPlot,
 ParametricPlot3D[
 x, u, model["Function"][x, u], u, 0, 80, PlotStyle -> Purple]
 ],
 x, 6, 0, 10, Appearance -> "Labeled"
 ]

You can also project back into 2D (Plot). For example, keeping y fixed and letting x vary.

yMax = Ceiling@*Max@Join[dat1, dat2][[All, 2]];
Manipulate[
 Show[
 ListPlot[dat1, dat2, PlotMarkers -> "OpenMarkers", 
 PlotTheme -> "Detailed"],
 Plot[yMax model["Function"][x, y], x, 0, 10, PlotStyle -> Purple, 
 Exclusions -> None]
 ],
 y, 40, 0, yMax, Appearance -> "Labeled"
 ]

Hope this helps.