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Coursera Machine Learning Recap: W1-2

2021-06-28

LZN

technology

cs229 coursera
W1

Notes
- “Batch” Gradient Descent: Each step use all training examples.
W2

Notes

Multivar linear regression
- learning rate alpha choices: 0.001, 0.003, 0.01, 0.03, 0.1, 0.3, 1… 3X test
- quadratic function / cubic function / third-order term
- polynomial regression
- Matrix inverse computational complexity O(n^3)
- Normal equation, use when feature numbers less than 10000 or so.
- Gradient descent complexity O(kn^2), where k is the number of iterations (epochs)
- Non-invertable matrix (singular/degenerate)
  - Cause 1: Redundent feature with linear relationship
  - Cause 2: Too many features (m«n)
- Clunkier 笨重的
Octave
- v= 1:0.1:2 arithmetic sequence
- ones(2,3) zeros(2,3) eye(3) rand(2,3)
- randn(2,3) % Guassian Distribution ~N(0,1)
- size(X), size(X,1), size(X,2)
- load(featureX.dat) % load a data file
- whos % command gives you variables in the current scope.
- clear X % delete X variable
- v = X(1:10) % slice
- save test.mat v % save v into test.mat file
- save test.txt v -ascii
- clear % clear all variables
- A=[A, [2; 4]] % matrix append a col vector to the rightmost position of A
- A(:) % squeeze nd data to 1d
- C = [A B]; C=[A;B] % right append and bottom append
- AB; A.B; % matrix multiplication and element-wise multiplication
- log(A); exp(A) % element-wise operation
- v + ones(length(v),1)
- [val, ind] = max(a)
- find(a<3) % return index
- [nrow, ncol]=find(A >= 7)
- sum(a); prod(a); % prod is the product of each element in a
- floor(a); ceil((a);
- max(A,[],1) % get max in each column
- max(A,[],2) % get max in each row
- sum(A, 1); sum(A,2); % column sum and row sum
- filpud(A) % FLIPUD Flip array in up/down direction.
- Plot
  - plot(t,y1); hold on; plot(t,y2); xlabel(‘time’); ylabel(‘val’); legend(‘sin’, ‘cos’)
  - print -dpng ‘myplot.png’
  - figure(1); plot(t,y1)
  - figure(2); plot(t,y2)
  - subplot(1,2,1); % divides canvas into 1x2 subdomain and access the first domain.
  - subplot(1,2,1); plot(t,y1); subplot(1,2,2); plot(t,y2)
  - axis([0.5 1 -1 1]) % set X axis from 0.5 to 1, and Y axis from -1 to 1
  - imagesc(A) % plot the matrix in heatmap
  - imagesc(A), colorbar, colormap gray; % in gray scale
  - comma chaining of commands would echo the result.
- Control
  - for i=1:10, v(i)=i^2; end;
  - ind = 1:10; for i=ind,
  - if elseif else
  - function y = f(x); y = x^2;
  - function y1, y2 = f(x) % multiple values return
- summation n. 求和
- reciprocal 倒数
- apostrophe 撇号
Programming

Carefully follow the matrix size after each vectorized operation, of particular caution is that matlab would perform add/subtract between 1-by-n and n-by-1 matrix by broadcasting the matrix to fit n-by-n size.
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echo in bashrc? No! bash_profile please.

2021-06-27

LZN

technology

linux python requriments.txt
- Reference
I tried to echo some message in the ~/.bashrc file, and then found scp does not work. In short, this is basically scp command try to parse these chars.

The scp command builds ssh connection first between the source and sink host, and ssh will execute source ~/.bashrc before full setup shell.

Therefore, put messages in ~/.bash_profile.

Reference
- https://chuacw.ath.cx/development/b/chuacw/archive/2019/02/04/how-the-scp-protocol-works.aspx
- https://huster.top/htmls/319.html (Chinese)
Updated 2021-06-27**
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python requirements.txt

2021-02-20

LZN

technology

linux python requriments.txt
- Refrence
To transfer a python project from one platform to another, we need to consider the dependencies. Using requirements.txt can solve this problem.

Start a new conda enviroment:
```
conda create -n test_aeolus python=3.6
conda env list
conda activate test_aeolus
```
Try to run the project in this new environment, you will encontour many ModuleNotFoundError.

This is a “ping-pang” game, and install required packages in the brand-new environment by pip, until our package runs smooth. At this moment:
```
pip freeze > requirements.txt
cat requirements.txt

certifi==2020.12.5
cftime==1.4.1
netCDF4==1.5.6
numpy==1.19.5
pandas==1.1.5
python-dateutil==2.8.1
pytz==2021.1
scipy==1.5.4
six==1.15.0
wrapt==1.12.1
wrf-python==1.3.1
xarray==0.16.2
```
Test requirements.txt:
```
conda deactivate
conda create -n test_install python=3.6
conda activate test_install
pip install -r requirements.txt
python run.py
```
After testing, you may want to delete the test environment
```
conda remove -n test_install --all
```
Refrence

https://blog.usejournal.com/why-and-how-to-make-a-requirements-txt-f329c685181e

Updated 2021-02-20**
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Welcome to LZN's Blog!

Coursera Machine Learning Recap: W1-2

W1

Notes

W2

Notes

Multivar linear regression

Octave

Programming

echo in bashrc? No! bash_profile please.

Reference

python requirements.txt

Refrence