пятница, 31 марта 2017 г.

1. Elefant
[x,y,z] = meshgrid(-2:0.1:2);
F=(x.^2 + y.^2 + z.^2) - 3;
isosurface(x,y,z,F,0);
F=(x.^3 + y.^3 + z.^3) -2;
isosurface(x,y,z,F,0);
axis auto
axis([-3, +3 , -3, +3, -3, +3]);

2.
[x,y,z] = meshgrid(-2:0.1:2);
F=x.^2 + y.^2 + z.^2 -3;
isosurface(x,y,z,F,0);
F=x.^2 + y.^3 + z.^2 -3;
isosurface(x,y,z,F,0);
F=x.^2 + y.^3 + z.^2 +3;
isosurface(x,y,z,F,0);
axis([-2.5, +2.5 , -2.5, +2.5,-2.5, +2.5]);

% axis auto

3.
clc
[x,y,z] = meshgrid(-2:0.1:2);
F=x.^4 + y.^4 + z.^4 - (x.^2 + y.^2 + z.^2);
% surface(x,y,z,F,0)
% isosurface(x,y,z,F,0)
hpatch = patch(isosurface(x,y,z,F,0));
isonormals(x,y,z,F,hpatch); %посчитаем нормали для красоты
set(hpatch,'FaceColor','r','EdgeColor','none'); %немного цвета для красоты

camlight left; lighting phong; %немного света для красоты

Simple graphics examples for Matlab

1.
N = 50;

NgrStr = 6;
NgrColumn = 1;

y1 = randn(N,1);

stat.mean = mean(y);
stat.std = std(y);
stat.median = median(y);

y2 = filter([ 1 1]/2,1,y);

t = linspace(0, 0.01, N);

subplot(NgrStr,NgrColumn,1);

plot(t,y1);
grid on;

subplot(NgrStr,NgrColumn,2);

plot(t,y2);
grid on;

subplot(NgrStr,1,3);
plot(y,'DisplayName','y');hold on;plot(y2,'DisplayName','y2');hold off;
grid on;

subplot(NgrStr,NgrColumn,4);

plot(y1,y2);
grid on;

subplot(NgrStr,NgrColumn,5);
stem(y2-y1);
grid on;

subplot(NgrStr,NgrColumn,6);
hist(y2-y1);
grid on;

.Net Assembly from MatLab

.Net Assembly from MatLab

1.
NET.addAssembly('System.Speech');
ss = System.Speech.Synthesis.SpeechSynthesizer;
ss.Volume = 100;
Speak(ss,'You can use .NET Library in Matrix Laboratory');

2.
asmpath = 'D:\VC\1305\gs.trade\GS.Matlab\bin\Debug\';
asmname = 'GS.Matlab.dll';

asm = NET.addAssembly(fullfile(asmpath,asmname));

obj = GS.Matlab.MyGraph;

mlData = cell(obj.getNewData)
objArr = cell(obj.getObjectArray);

objNewDataArr = obj.getNewDataProp;

figure('Name',char(mlData{1}))
plot(double(mlData{2}(2)))

xlabel(char(mlData{2}(1)))

3.
% F:\Work\Math\Matlab\Net
% NetDocCell_01.m

R = 3;
C=1;
asmpath = 'D:\VC\1305\gs.trade\GS.Matlab\bin\Debug\';
asmname = 'GS.Matlab.dll';

asm = NET.addAssembly(fullfile(asmpath,asmname));

obj = GS.Matlab.MyGraph;

mlData = cell(obj.getNewData);
%objArr = cell(obj.getObjectArray);
objNewDataArr = obj.getNewDataProp;

figure('Name',char(mlData{1}));
subplot(R,C,1);
% figure('Name',char(mlData{1}));
plot(double(mlData{2}(2)));
xlabel(char(mlData{2}(1)));

subplot(R,C,2);
objArr = obj.getObjectArray();

doubles1 = double(objArr(1));
doubles2 = double(objArr(2));

plot([doubles1 doubles2]);

subplot(R,C,3);
objDouble = obj.getDoubleArray;
doubles = double(objDouble);

plot(doubles);

------------------------------------------------------------

.cs
D:\VC\1305\gs.trade\GS.MAtlab\
namespace GS.Matlab
{
// Call Methods from .Net Assembly from Matlab
public class MyGraph
{
public Object[] getNewData()
/*
* Create a System.Object array to use in MATLAB examples.
* Returns containerArr System.Object array containing:
* fLabel System.String object
* plotData System.Object array containing:
* xLabel System.String object
* doubleArr System.Double array
*/
{
String fLabel = "Figure Showing New Graph Data";
Double[] doubleArr =
{
18, 32, 3.133, 44, -9.9, -13, 33.03
};
String xLabel = "X-Axis Label";
Object[] plotData = {xLabel, doubleArr};
Object[] containerArr = {fLabel, plotData};
return containerArr;
}

public Object[] getNewDataProp {
get
{
String fLabel = "Figure Showing New Graph Data";
Double[] doubleArr =
{
18, 32, 3.133, 44, -9.9, -13, 33.03
};
String xLabel = "X-Axis Label";
Object[] plotData = {xLabel, doubleArr};
Object[] containerArr = {fLabel, plotData};
return containerArr;
}
}

public object[] getObjectArray()
{
var arr1 = new double[] {1, 2, 3, 4, 5};
var arr2 = new double[] { 6, 7, 8, 9, 10 };
return new object[] {arr1, arr2};
}
public double[] getDoubleArray()
{
return new double[] { 1, 2, 3, 4, 5 };
}

public object[] getObjectArrayProp => new object[]
{
new double[] {1,2,3},
new double[] {4,5,6,7,8,9}
};
public double[] getDoubleArrayProp => new double[] { 1, 2, 3, 4, 5 };
}

}

MCR MPS

http://www.mathworks.com/help/mps/server/creating-a-server.html

воскресенье, 26 марта 2017 г.

Track

R=2;C=2;

vz = 10; % velocity constant
a = -32; % acceleration constant

subplot(R,C,1);

t = 0:.1:1;
z = vz*t;
% z = vz*t + 1/2*a*t.^2;

vx = 2;
x = vx*t;
%x = vx*t + 1/2*a*t.^2;

vy = 3;
y = vy*t;
% y = vy*t + 1/2*a*t.^2;

u = gradient(x);
v = gradient(y);
w = gradient(z);
scale = 0;

quiver3(x,y,z,u,v,w,scale);
view([70,18]);

subplot(R,C,2)
% z = vz*t;
z = vz*t + 1/2*a*t.^2;

vx = 2;
x = vx*t;
% x = vx*t + 1/2*a*t.^2;

vy = 3;
y = vy*t;
% y = vy*t + 1/2*a*t.^2;

u = gradient(x);
v = gradient(y);
w = gradient(z);
scale = 0;

quiver3(x,y,z,u,v,w,scale);
view([70,18]);

subplot(R,C,3)
% z = vz*t;
z = vz*t + 1/2*a*t.^2;

vx = 2;
% x = vx*t;
x = vx*t + 1/2*a*t.^2;

vy = 3;
y = vy*t;
% y = vy*t + 1/2*a*t.^2;

u = gradient(x);
v = gradient(y);
w = gradient(z);
scale = 0;

quiver3(x,y,z,u,v,w,scale);
view([70,18]);

subplot(R,C,4)
% z = vz*t;
z = vz*t + 1/2*a*t.^2;

vx = 2;
% x = vx*t;
x = vx*t + 1/2*a*t.^2;

vy = 3;
%y = vy*t;
y = vy*t + 1/2*a*t.^2;

u = gradient(x);
v = gradient(y);
w = gradient(z);
scale = 0;

quiver3(x,y,z,u,v,w,scale);
view([70,18]);

Combine Stem and Line

x = linspace(0,2*pi,60);
a = sin(x);
b = cos(x);
stem(x,a+b);

hold on
plot(x,a)
plot(x,b)
hold off

legend('a+b','a = sin(x)','b = cos(x)')
xlabel('Time in \musecs')
ylabel('Magnitude')
title('Linear Combination of Two Functions');

Include Loop Variable Value in Graph Title

x = linspace(0,10,100);
for k = 1:4
subplot(2,2,k);
yk = sin(k*x);
plot(x,yk)
title(['y = sin(' num2str(k) 'x)'])
end

Legend Mean Value + Text Signs

R=5;C=1;
dat = rand(50,1);

subplot(R,C,1);
plot(dat)
m = mean(dat);
ax = gca;
xlimits = ax.XLim;
h = line([xlimits(1),xlimits(2)],[m,m],'Color','k','LineStyle','--');

legend(h,'mean of data');

subplot(R,C,2);

g1 = hggroup;
g2 = hggroup;
t = linspace(0,2*pi,100);
plot(t,sin(t),'b','Parent',g1)
hold on
plot(t,sin(t+1/7),'b','Parent',g1)
plot(t,sin(t+2/7),'b','Parent',g1)
plot(t,sin(t+3/7),'b','Parent',g1)
plot(t,cos(t),'g','Parent',g2)
plot(t,cos(t+1/7),'g','Parent',g2)
plot(t,cos(t+2/7),'g','Parent',g2)
plot(t,cos(t+3/7),'g','Parent',g2)
hold off % reset hold state to off

legend([g1,g2],'sine','cosine')

subplot(R,C,3);
x = linspace(0,2*pi,100);
y1 = sin(x);
p1 = plot(x,y1,'DisplayName','sin(x)');
hold on
y2 = sin(x) + pi/2;
p2 = plot(x,y2,'DisplayName','sin(x) + \pi/2');
y3 = sin(x) + pi;
p3 = plot(x,y3,'DisplayName','sin(x) + \pi');
hold off

legend([p1 p2 p3])

subplot(R,C,4);

t = linspace(0,2*pi,50);
y = sin(t);
plot(t,y);

x1 = pi;
y1 = sin(pi);
str1 = '\leftarrow sin(\pi) = 0';
text(x1,y1,str1);

x2 = 3*pi/4;
y2 = sin(3*pi/4);
str2 = '\leftarrow sin(3\pi/4) = 0.71';
text(x2,y2,str2)
x3 = 5*pi/4;
y3 = sin(5*pi/4);
str3 = 'sin(5\pi/4) = -0.71 \rightarrow';

text(x3,y3,str3,'HorizontalAlignment','right');

subplot(R,C,5);

x = linspace(-3,3);
y = (x/5-x.^3).*exp(-2*x.^2);
plot(x,y);

indexmin = find(min(y) == y);
xmin = x(indexmin);
ymin = y(indexmin);
indexmax = find(max(y) == y);
xmax = x(indexmax);
ymax = y(indexmax);

strmin = ['Minimum = ',num2str(ymin)];
text(xmin,ymin,strmin,'HorizontalAlignment','left');
strmax = ['Maximum = ',num2str(ymax)];
text(xmax,ymax,strmax,'HorizontalAlignment','right');

Graph with Two y-Axes

1.
R = 5; C = 1;

subplot(R,C,1);
x = linspace(0,2*pi,25);

y1 = sin(x);
% y2 = 0.5*sin(x);
y2 = exp(-1/3*x).*sin(x);
plot(x,y1);
grid on

hold on

stem(x,y2);

hold off

subplot(R,C,2);

A = 1000;
a = 0.005;
b = 0.005;
t = 0:900;
z1 = A*exp(-a*t);
z2 = sin(b*t);

[ax,p1,p2] = plotyy(t,z1,t,z2,'semilogy','plot');
ylabel(ax(1),'Semilog Plot') % label left y-axis
ylabel(ax(2),'Linear Plot') % label right y-axis
xlabel(ax(2),'Time') % label x-axis

p1.LineStyle = '--';
p1.LineWidth = 2;
p2.LineWidth = 2;

grid(ax(1),'on')

пятница, 24 марта 2017 г.

Call .Net Methods from ML

1.
R = 3;
C=1;
asmpath = 'D:\VC\1305\gs.trade\GS.Matlab\bin\Debug\';
asmname = 'GS.Matlab.dll';

asm = NET.addAssembly(fullfile(asmpath,asmname));

obj = GS.Matlab.MyGraph;

mlData = cell(obj.getNewData);
%objArr = cell(obj.getObjectArray);
objNewDataArr = obj.getNewDataProp;

figure('Name',char(mlData{1}));
subplot(R,C,1);
% figure('Name',char(mlData{1}));
plot(double(mlData{2}(2)));
xlabel(char(mlData{2}(1)));

subplot(R,C,2);
objArr = obj.getObjectArray();

doubles1 = double(objArr(1));
doubles2 = double(objArr(2));

plot([doubles1 doubles2]);

subplot(R,C,3);
objDouble = obj.getDoubleArray;
doubles = double(objDouble);
plot(doubles);

webread matlab and Net classes

webRead

https://www.mathworks.com/help/matlab/ref/webread.html

Convert .NET Arrays to Cell Arrays

https://www.mathworks.com/help/matlab/matlab_external/net-arrays-to-cell-arrays.html

Pass Cell Arrays of .NET Data

https://www.mathworks.com/help/matlab/matlab_external/tips-for-working-with-cell-arrays-of-net-data.html

Handle Data Returned from .NET Objects

https://www.mathworks.com/help/matlab/matlab_external/handling-net-data-in-matlab_bte9owt-1.html#bte9paq-1

Access a Simple .NET Class

https://www.mathworks.com/help/matlab/matlab_external/access-a-simple-net-class.html

Call .NET Methods With `ref` Keyword

https://www.mathworks.com/help/matlab/matlab_external/call-net-methods-with-ref-keyword.html

Calling .NET Methods

matlab_external/calling-net-methods.html?s_tid=gn_loc_drop

Use .NET methods in MATLAB®, method signatures, arguments by reference, optional arguments

https://www.mathworks.com/help/matlab/methods-.html

пятница, 31 марта 2017 г.

Classification Examples(fisheriris)

Classification Probability upon "load fisheriris"

Getting Started with Statistics and Machine Learning Toolbox

Machine Learning for Predictive Modelling

Machine Learning with MATLAB: Getting Started with Classification

Applying Multivariate Classification in the Life Sciences with Statistics Toolbox

An Introduction to Classification

Examples Machine Learning with MATLAB

Machine Learning Made Easy

Signal Processing and Machine Learning Techniques for Sensor Data Analytics

Supervised Learning Workflow and Algorithms

Data-Driven Insights with MATLAB Analytics: An Energy Load Forecasting Case Study

MACHINE LEARNINGS Examples

Classification Learner

четверг, 30 марта 2017 г.

Using MATLAB to Develop and Deploy Financial Models

Using MATLAB to Optimize Portfolios with Financial Toolbox

Analyzing Investment Strategies with CVaR Portfolio Optimization in MATLAB

AlexNet

AlexNet

Параллельные вычисления при моделировании с серией параметров

среда, 29 марта 2017 г.

Интеграция MATLAB с Microsoft Visual Studio

Онлайн оценка параметров модели Online Parameter Estimation

вторник, 28 марта 2017 г.

воскресенье, 26 марта 2017 г.

пятница, 24 марта 2017 г.