Hello, I have this array:
Class1 = [1 0; 0.5 1; 0.5 1.5];How do I go about plotting the 3 points from Class1? If I use
plot(Class1)I do not get the correct plot.
MATLAB: Plot graph from array
arrayMATLABplot
Related Solutions
I don't have knnclassify(). Perhaps you have an old version, a custom function, or a different toolbox than me. I have knnsearch(). Attached is a demo script showing knnsearch() to classify (x,y) coordinates based on how close they are to training classes. Perhaps it will be useful for you.
% Demo to illustrate K Nearest Neighbor classification for (x,y) coordinate points.
% Setup, clean up, and initialization:
clc; % Clear the command window.
close all; % Close all figures (except those of imtool.)
clear; % Erase all existing variables. Or clearvars if you want.
workspace; % Make sure the workspace panel is showing.
format long g;format compact;fontSize = 20;markerSize = 10;lineWidth = 2;numTrainingPoints = 10;% Make up coordinates that we'll define as class 1.
trainingCoords1 = rand(numTrainingPoints, 2) + 1;% Make up coordinates that we'll define as class 2.
trainingCoords2 = rand(numTrainingPoints, 2) * 1.75 + 4;% Plot both classes of training points.
subplot(2, 2, 1);plot(trainingCoords1(:, 1), trainingCoords1(:, 2), 'rs', 'LineWidth', lineWidth, 'MarkerSize', markerSize);hold on;plot(trainingCoords2(:, 1), trainingCoords2(:, 2), 'b^', 'LineWidth', lineWidth, 'MarkerSize', markerSize);grid on;xlim([0, 6]);ylim([0, 6]);title('Training data. Points have known, defined class.', 'FontSize', fontSize, 'Interpreter', 'None');legend('Class 1', 'Class 2', 'Location', 'northwest');% Enlarge figure to full screen.
set(gcf, 'Units', 'Normalized', 'Outerposition', [0, 0, 1, 1]);% Now make up unknown data
numUnknownPoints = 50;unknownCoords = 3 * (rand(numUnknownPoints, 2) - 0.5) + 3;% Plot the test data points with as-of-yet unknown class all in black asterisks.
subplot(2, 2, 2);plot(unknownCoords(:, 1), unknownCoords(:, 2), 'k*', 'LineWidth', lineWidth, 'MarkerSize', markerSize);grid on;xlim([0, 6]);ylim([0, 6]);legend('As-of-yet Unknown Class', 'Location', 'northwest');title('Test Data Before Classification', 'FontSize', fontSize, 'Interpreter', 'None');% Get the classes of the unknown data.
% First collect all the training data into one tall array
trainingCoords = [trainingCoords1; trainingCoords2];[indexes, distancesOfTheIndexes] = knnsearch(trainingCoords, unknownCoords, ... 'NSMethod', 'exhaustive',... 'k', 5,... % Get indexes of the 5 nearest points 'distance', 'euclidean'); % Regular Pythagorean formula for distance
% Extract the classes
% The way I defined the classes is if the index is <= numTrainingPoints, it's class 1, otherwise it's class 2.
class1Map = indexes <= numTrainingPoints;class2Map = indexes > numTrainingPoints;% Now sum the maps horizontally to count the number of each class that was found:
class1Count = sum(class1Map, 2);class2Count = sum(class2Map, 2);% Now determine which rows got more class 1 "votes"
% so we'll know whether this coordinate of our unknown test data
% "belongs" to class 1 or class 2.
itIsClass1 = class1Count >= class2Count;itIsClass2 = class1Count < class2Count;% Extract the test coordinates into two arrays, one for class 1 and one for class 2.
class1 = unknownCoords(itIsClass1, :);class2 = unknownCoords(itIsClass2, :);% Now plot what we found. Each class gets the same marker as the training class.
subplot(2, 2, 4);plot(class1(:, 1), class1(:, 2), 'rs', 'LineWidth', lineWidth, 'MarkerSize', markerSize);hold on;plot(class2(:, 1), class2(:, 2), 'b^', 'LineWidth', lineWidth, 'MarkerSize', markerSize);grid on;xlim([0, 6]);ylim([0, 6]);title('Test Data After Classification', 'FontSize', fontSize, 'Interpreter', 'None');legend('Estimated to be in Class 1', 'Estimated to be in Class 2', 'Location', 'northwest');
close all, clear all, clc, plt=0;
[x,t] = simplefit_dataset;
net = fitnet(10);
rng(0)
[net tr ] = train(net, x, t);
plt = plt+1, figure(plt), hold on;
plot(log(tr.perf),'b', 'LineWidth', 2)
% plot(log(tr.vperf),'g', 'LineWidth', 2)
plot(log(tr.tperf),'r', 'LineWidth', 2)
Thank you for formally accepting my answer
Greg
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