程序代写代做代考 matlab Clarify on Assignment-1
Clarify on Assignment-1
Matlab Functions and Tools
• Some functions are given. There is no need to
implement them. Please refer to the links in
the next slide.
• You can also use other resources, with proper
citation in your report.
Useful Matlab Functions
• Naïve Bayes classifier
– PredictClass = classify(Xtest,Xtrain,Ytrain,’diaglinear’);
• Randomly split data
– p = randperm(n,k)
– Indices = crossvalind(‘Kfold’, N, K)
• plotImages
– plotImages(digitsImages, xy_coord, scale, skip);
• LLE:
– http://www.cs.nyu.edu/~roweis/lle/code.html
• ISOMAP:
– http://web.mit.edu/cocosci/isomap/isomap.html
• LDA-dimension reduction
– http://lvdmaaten.github.io/drtoolbox/
decide the
model to learn
n: total # of samples
k: select k samples by permutation
http://www.cs.nyu.edu/~roweis/lle/code.html
http://isomap.stanford.edu/
http://isomap.stanford.edu/
http://homepage.tudelft.nl/19j49/Matlab_Toolbox_for_Dimensionality_Reduction.html
http://homepage.tudelft.nl/19j49/Matlab_Toolbox_for_Dimensionality_Reduction.html
Datasets
• Dataset A (record activity sensors):
– Sample-feature matrix: fea (19,000 x 81)
• Features: readings of 81 sensors
• The data is in time-series, given in time order
– Missing values
• ‘NaN’
– Outliers
• Negative readings are not outliers
Datasets
• Dataset B (image data of handwritten digits)
– Sample-feature matrix: fea (2066 x 784)
• Features: 28 x 28 gray-scale images, in column-wise
– Ground truth labels: gnd (2066 x 1)
• Labels: 0, 1, 2, 3, 4
Datasets
• Dataset C (clinic data)
– Sample-feature matrix:
• fea (2100 x 21)
• Need to be normalized (min-max) before further
processing
– Ground truth labels
• gnd (2100 x 1)
• 3 classes: normal(1), suspect(2), pathologic(3)
Q1: Data Cleaning and Preprocessing
• Missing values; Outliers
– Detect and fix them
• Normalization:
– Min-max
– Z-score
• Plot histograms
• Observations and comments!
Q2&3: Feature Extraction
• Linear methods:
– PCA
– LDA
• Nonlinear methods:
– LLE
– ISOMAP
• Supervised vs. non-supervised dimensionality
reduction
Q4: Feature Selection
• Search strategy
– SFS
– SBS
• Objective function
– Filter based
– Wrapper based
basics
• ‘Hello World!’
– a = 3;
– b = 4;
– c = a + b
• end each statement with semicolon, if you do
not like to see the result in the command
window
basics
• arithmetic operators:
– addition: A+B
– subtraction: A-B
– multiplication: A*B
– right division: A/B = A*inv(B)
– left division: AB = inv(A)*B
– power: A^b
– transpose: A’
– colon operator:
• to create vectors: a:b
• array subscripting: A(:,b)
basics
• dot operators (a.k.a element-wise operators)
A.*B, A./B, A.B, and A.^B
• relational operators
a
• logical operations
a||b (or), a&&b (and), ~ a(not)
• element-wise logical operators
A|B, A&B, ~A
basics
• operator precedence
– Parentheses
– transpose and power
– unary plus, unary minus, and logical negation
– multiplication(s) and division(s)
– addition and subtract
– …
basics
• flow control
– conditional control
• if, else, and elseif
• switch and case
– loop control
• for
• while
• break
• continue
basics
• if
if expression1
statements1
elseif expression2
statements2
else
statements3
end
basics
• for
for index = values
program statements
end
• while
while expression
program statements
end
basics
• function definition
– function [output_variables] =
fcn_name(input_variables)
– the name of a function should be consistent with
the file name
an example
• Given the corresponding coefficients of two lines
(ax+by+c=0), calculate the intersection point and
plot the lines on a figure.
• Function:
[intersection,Runtime] = myPlot(line1,line2)
• Script to call this function:
clear all;clc
load coeffs
line1 = coeff(1,:);
line2 = coeff(2,:);
[intersection,RunTime] = myPlot(line1,line2)
how to access?
• have a license
• Nexus computers
– on campus
– remotely
• Octave
help
• where to look for answers?
– Matlab Help
– Mathworks website
– Online forums
– TAs
refs
• www.mathworks.com
• www.gnu.org/software/octave/
• saw.uwaterloo.ca/matlab/
