CourseraのMachine Learningのweek2に出てくるOctaveの文法まとめ
はじめに
最近人気の CourseraのMachine Learning を勉強し始めました｡
この講座の中ではOctaveかMATLABを使うのですが､初めて使う言語なので勉強のために文法をメモしてみます｡
僕はMATLABを使用することにしました｡
Basic Operations
% はコメント
% 加減乗除: +, -, *, /
>> 2^6 % 累乗
ans =
64
>> 1 == 2 % false
ans =
logical
0
>> 1 ~= 2 % true
ans =
logical
1
>> 1 && 0
ans =
logical
0
>> 1 || 0
ans =
logical
1
>> xor(1,0)
ans =
logical
1
>> a = 3; % セミコロンをつけると出力しない
>> b = 'hi';
>> a = pi;
>> disp(a);
3.1416
>> disp(sprintf('2 decimals: %0.2f', a))
2 decimals: 3.14
>> format long
>> disp(a);
3.141592653589793
>> format short
>> disp(a);
3.1416
>> A = [1 2; 3 4; 5 6]
A =
1 2
3 4
5 6
>> A = [1 2;
3 4;
5 6] % このような書き方もできる
A =
1 2
3 4
5 6
>> v = [1; 2; 3]
v =
1
2
3
>> v = 1:0.1:2
v =
1.0000 1.1000 1.2000 1.3000 1.4000 1.5000 1.6000 1.7000 1.8000 1.9000 2.0000
>> v = 1:6
v =
1 2 3 4 5 6
>> ones(2,3)
ans =
1 1 1
1 1 1
>> C = 2*ones(2,3)
C =
2 2 2
2 2 2
>> w = ones(1, 3)
w =
1 1 1
>> w = zeros(1, 3)
w =
0 0 0
>> w = rand(3, 3)
w =
0.9040 0.2420 0.8128
0.9409 0.9757 0.6974
0.8025 0.3172 0.2695
>> w = randn(1, 3)
w =
0.5371 -0.3804 -1.5310
>> w = -6 + sqrt(10)*(randn(1,10000));
>> hist(w) % histgramを表示
>> hist(w, 50)
>> I = eye(4)
I =
1 0 0 0
0 1 0 0
0 0 1 0
0 0 0 1
Moving Data Around
>> disp(A)
1 2
3 4
5 6
>> sz = size(A)
sz =
3 2
>> size(sz)
ans =
1 2
>> size(A,1)
ans =
3
>> size(A,2)
ans =
2
>> a = [1 2 3 4]
a =
1 2 3 4
>> length(a)
ans =
4
>> length(A)
ans =
3
>> length([1; 2; 3; 4; 5])
ans =
5
>> load featuresX.dat % ファイルの読み込み
>> who
Your variables are:
A a ans sz
>> whos
Name Size Bytes Class Attributes
A 3x2 48 double
a 1x4 32 double
ans 1x1 8 double
sz 1x2 16 double
>> clear
>> who
>> A = [1 2; 3 4; 5 6]
A =
1 2
3 4
5 6
>> save hello.mat A -ascii % save as text (ASCII)
>> A(3, 2)
ans =
6
>> A(2, :) % ":" means every element along that row/column
ans =
3 4
>> A(:, 2)
ans =
2
4
6
>> A([1 3], :)
ans =
1 2
5 6
>> A(:, 2) = [10; 11; 12]
A =
1 10
3 11
5 12
>> A = [A, [100; 101; 102]]
A =
1 10 100
3 11 101
5 12 102
>> A(:)
ans =
1
3
5
10
11
12
100
101
102
>> B = [1 2; 3 4; 5 6]
B =
1 2
3 4
5 6
>> C = [11 12; 13 14; 15 16]
C =
11 12
13 14
15 16
>> D = [B C]
D =
1 2 11 12
3 4 13 14
5 6 15 16
>> E = [B; C]
E =
1 2
3 4
5 6
11 12
13 14
15 16
>> size(E)
ans =
6 2
Computing on Data
>> disp(A)
1 2
3 4
5 6
>> disp(B)
11 12
13 14
15 16
>> disp(C)
1 1
2 2
>> A * C
ans =
5 5
11 11
17 17
>> A .* B
ans =
11 24
39 56
75 96
>> A .^ 2
ans =
1 4
9 16
25 36
>> v
v =
1
2
3
>> 1 ./ v
ans =
1.0000
0.5000
0.3333
>> log(v)
ans =
0
0.6931
1.0986
>> exp(v)
ans =
2.7183
7.3891
20.0855
>> abs([-1; -2; -3])
ans =
1
2
3
>> v + ones(length(v), 1)
ans =
2
3
4
>> v + 1
ans =
2
3
4
>> A'
ans =
1 3 5
2 4 6
>> (A')'
ans =
1 2
3 4
5 6
>> max(A)
ans =
5 6
>> a = [1 15 2 0.5]
a =
1.0000 15.0000 2.0000 0.5000
>> [val, ind] = max(a)
val =
15
ind =
2
>> a < 3
ans =
1×4 logical array
1 0 1 1
>> find(a < 3)
ans =
1 3 4
>> A = magic(3)
A =
8 1 6
3 5 7
4 9 2
>> [row, column] = find(A >= 7)
row =
1
3
2
column =
1
2
3
>> a
a =
1.0000 15.0000 2.0000 0.5000
>> sum(a)
ans =
18.5000
>> prod(a) % 掛ける
ans =
15
>> floor(a)
ans =
1 15 2 0
>> ceil(a)
ans =
1 15 2 1
>> max(rand(3), rand(3))
ans =
0.9040 0.7098 0.8128
0.9409 0.9757 0.7385
0.8025 0.3172 0.9410
>> A
A =
8 1 6
3 5 7
4 9 2
>> max(A)
ans =
8 9 7
>> max(A,[],1)
ans =
8 9 7
>> max(A,[],2)
ans =
8
7
9
>> max(max(A))
ans =
9
>> max(A(:))
ans =
9
>> A = magic(9)
A =
47 58 69 80 1 12 23 34 45
57 68 79 9 11 22 33 44 46
67 78 8 10 21 32 43 54 56
77 7 18 20 31 42 53 55 66
6 17 19 30 41 52 63 65 76
16 27 29 40 51 62 64 75 5
26 28 39 50 61 72 74 4 15
36 38 49 60 71 73 3 14 25
37 48 59 70 81 2 13 24 35
>> sum(A,1)
ans =
369 369 369 369 369 369 369 369 369
>> sum(A,2)
ans =
369
369
369
369
369
369
369
369
369
>> eye(9)
ans =
1 0 0 0 0 0 0 0 0
0 1 0 0 0 0 0 0 0
0 0 1 0 0 0 0 0 0
0 0 0 1 0 0 0 0 0
0 0 0 0 1 0 0 0 0
0 0 0 0 0 1 0 0 0
0 0 0 0 0 0 1 0 0
0 0 0 0 0 0 0 1 0
0 0 0 0 0 0 0 0 1
>> A .* eye(9)
ans =
47 0 0 0 0 0 0 0 0
0 68 0 0 0 0 0 0 0
0 0 8 0 0 0 0 0 0
0 0 0 20 0 0 0 0 0
0 0 0 0 41 0 0 0 0
0 0 0 0 0 62 0 0 0
0 0 0 0 0 0 74 0 0
0 0 0 0 0 0 0 14 0
0 0 0 0 0 0 0 0 35
>> sum(A.*eye(9))
ans =
47 68 8 20 41 62 74 14 35
>> sum(sum(A.*eye(9)))
ans =
369
>> flipud(eye(9))
ans =
0 0 0 0 0 0 0 0 1
0 0 0 0 0 0 0 1 0
0 0 0 0 0 0 1 0 0
0 0 0 0 0 1 0 0 0
0 0 0 0 1 0 0 0 0
0 0 0 1 0 0 0 0 0
0 0 1 0 0 0 0 0 0
0 1 0 0 0 0 0 0 0
1 0 0 0 0 0 0 0 0
>> sum(sum(A.*flipud(eye(9))))
ans =
369
>> A = magic(3)
A =
8 1 6
3 5 7
4 9 2
>> temp = pinv(A)
temp =
0.1472 -0.1444 0.0639
-0.0611 0.0222 0.1056
-0.0194 0.1889 -0.1028
>> temp * A
ans =
1.0000 0.0000 -0.0000
-0.0000 1.0000 0.0000
0.0000 -0.0000 1.0000
Plotting Data
>> t = [0:0.01:0.98];
>> y1 = sin(2*pi*4*t);
>> plot(t,y1);
>> y2 = cos(2*pi*4*t);
>> plot(t,y2);
>> hold on; % 複数のプロットを同じ図に
>> plot(t,y1,'r');
>> xlabel('time');
>> ylabel('value');
>> legend('cos', 'sin');
>> title('my plot');
>> print -dpng 'myPlot.png'
>> close % 閉じる
>> figure(1); plot(t, y1); % 複数開く
>> figure(2); plot(t, y2);
>> subplot(1,2,1); % Divides plot a 1×2 grid. access first element.
>> plot(t, y1);
>> subplot(1,2,2);
>> plot(t, y2);
>> axis([0.5 1 -1 1])
>> clf;
>> A = magic(5)
A =
17 24 1 8 15
23 5 7 14 16
4 6 13 20 22
10 12 19 21 3
11 18 25 2 9
>> imagesc(A)
>> imagesc(magic(10)), colorbar, colormap gray;
Control Statements: for, while, if statement
>> v = zeros(10, 1)
v =
0
0
0
0
0
0
0
0
0
0
>> for i=1:10,
v(i) = 2^i;
end;
>> v
v =
2
4
8
16
32
64
128
256
512
1024
>> indices=1:10
indices =
1 2 3 4 5 6 7 8 9 10
>> for i=indices,
disp(i);
end;
1
2
3
4
5
6
7
8
9
10
>> v
v =
2
4
8
16
32
64
128
256
512
1024
>> while i <= 5,
v(i) = 100;
i = i + 1;
end;
>> v
v =
2
4
8
16
32
64
128
256
512
1024
>> i = 1
i =
1
>> while true,
v(i) = 999;
i = i + 1;
if i == 6,
break;
end;
end;
>> v
v =
999
999
999
999
999
64
128
256
512
1024
>> v(1) = 2;
>> if v(1) == 1,
disp('The value is one');
elseif v(1) == 2,
disp('The value is two');
else
disp('The value is not one or two.');
end
The value is two
Author And Source
この問題について(CourseraのMachine Learningのweek2に出てくるOctaveの文法まとめ), 我々は、より多くの情報をここで見つけました https://qiita.com/kyntk/items/78fabd60837753c1c1ce著者帰属：元の著者の情報は、元のURLに含まれています。著作権は原作者に属する。
Content is automatically searched and collected through network algorithms . If there is a violation . Please contact us . We will adjust (correct author information ,or delete content ) as soon as possible .