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14 changed files with 1614 additions and 17 deletions
3
.gitignore
vendored
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.gitignore
vendored
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*.asv
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.vscode
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*.pdf
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49
assignments/hwk01/Bayes_Learning.m
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49
assignments/hwk01/Bayes_Learning.m
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% implements Bayes_Learning, returns the outputs (p1: learned Bernoulli
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% parameters of the first class, p2: learned Bernoulli parameters of the
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% second class, pc1: best prior of the first class, pc2: best prior of the
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% second class
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function [p1,p2,pc1,pc2] = Bayes_Learning(training_data, validation_data)
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[train_row_size, column_size] = size (training_data); % dimension of training data
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[valid_row_size, ~] = size (validation_data); % dimension of validation data
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X = training_data(1:train_row_size, 1:column_size-1); %Training data
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% (1) TODO: find label counts of class 1 and class 2
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% (2) TODO: get MLE p1, p2
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% Use different P(C_1) and P(C_2) on validation set
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% We compute g(x) = based on priors P(C_1), P(C_2), MLE estimator p1, p2, and x_{1*D}
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error_table = zeros(11,4); % build an error table with 4 columns of : sigma, P(C1), P(C2), error_rate
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index = 1; % row index of error table
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for sigma = [0.00001,0.0001,0.001,0.01,0.1,1,2,3,4,5,6]
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P_C1 = 1-(exp(-sigma)); % set priors using formula P(C1)=1-(exp(-sigma))
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P_C2 = 1 - P_C1;
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error_count = 0; % total number of errors to be count
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% (3) TODO: compute likelihood for class1 and class2 , then compute the posterior
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% probability for both classes (posterior = prior x likelihood).
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% Classify each validation sample as whichever class has the higher posterior probability.
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% If the sample is misclassified, increment the error count (error_count = error_count + 1);
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error_table(index,1) = sigma;
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error_table(index,2) = P_C1;
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error_table(index,3) = P_C2;
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error_table(index,4) = error_count/valid_row_size; % update error table
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index = index + 1;
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end
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% get the best priors
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[~, I] = min(error_table(:,4)); % find row index of the lowest error rate on validation set
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pc1 = error_table(I,2);
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pc2 = error_table(I,3); % best priors
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% print error table to terminal
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fprintf('\n Error rates of all priors on validation set: \n\n');
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fprintf(' sigma P(C1) P(C2) error rate on validation set\n\n');
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disp(error_table);
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end
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41
assignments/hwk01/Bayes_Testing.m
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assignments/hwk01/Bayes_Testing.m
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% implements Bayes Testing, return the test error (p1: learned Bernoulli
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% parameters of the first class, p2: learned Bernoulli parameters of the
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% second class; pc1: best prior of the first class, pc2: best prior of the
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% second class
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function test_error = Bayes_Testing(test_data, p1, p2, pc1, pc2)
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% (1) TODO: classify the test set using the learned parameters p1, p2, pc1, pc2
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[test_row_size, column_size] = size(test_data); % dimension of test data
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X = test_data(1:test_row_size, 1:column_size-1); % test data
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y = test_data(:,column_size); % test labels
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c = 0;
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for i = 1:test_row_size
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x = X(i, :);
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correct_label = y(i);
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postc1 = prod(p1 .^ x .* (1 - p1) .^ (1 - x)) * pc1;
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postc2 = prod(p2 .^ x .* (1 - p2) .^ (1 - x)) * pc2;
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if postc1 > postc2
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lab = 1;
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else
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lab = 2;
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end
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if lab == correct_label
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c = c + 1;
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end
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end
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test_error = (test_row_size - c) / test_row_size;
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% (2) TODO: compute error rate and print it
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% (test_error = # of incorrectly classified / total number of test samples
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fprintf('Error rate on the test dataset is: \n\n');
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disp(test_error);
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end
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25
assignments/hwk01/HW1.md
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assignments/hwk01/HW1.md
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---
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geometry: margin=2cm
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output: pdf_document
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---
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\renewcommand{\c}[1]{\textcolor{gray}{#1}}
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1. **(20 points)**
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\c{Derive the VC dimension of the following classifiers.}
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2.
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3. **(20 points)**
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\c{Let $P (x|C)$ denote a Bernoulli density function for a class $C \in {C_1, C_2}$
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and $P (C)$ denote the prior}
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a. \c{Given the priors $P (C_1)$ and $P (C_2)$, and the Bernoulli densities
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specified by $p_1 \equiv p(x = 0|C_1)$ and $p_2 \equiv p(x = 0|C_2)$, derive the
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classification rules for classifying a sample $x$ into $C_1$ and $C_2$ based on the
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posteriors $P (C_1|x)$ and $P (C_2|x)$. (Hint: give rules for classifying $x = 0$ and
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$x = 1$.)}
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For $x=0$, the posteriors $P(C_i | x)$ are given by $P(C_i | x = 0) = \frac{p(x = 0 | C_i) p(C_i)}{p(x = 0)}$.
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- $p(x = 0 | C_i)$ is given to us as $p_1$
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29
assignments/hwk01/HW1_script.m
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assignments/hwk01/HW1_script.m
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% solves question 1d, Print a table of error rate of each prior on the
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% validation set and the error rate using the best prior on the test set.
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% use functions MLE_Learning.m, Bayes_Learning.m, Bayes_Testing.m
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% load data
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load('training_data.txt');
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load('validation_data.txt');
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load('testing_data.txt');
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%Part 1: using the first two columns to test MLE_Learning and Bayes_Testing
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%function
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training2_data = training_data(:,[1,2,end]);
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testing2_data = testing_data(:,[1,2,end]);
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[p1,p2,pc1,pc2] = MLE_Learning(training2_data);
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test_error = Bayes_Testing(testing2_data, p1, p2, pc1, pc2); % use parameters to calculate test error
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%Part 2: using the compmlete dataset to test MLE_Learning
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[p1,p2,pc1,pc2] = MLE_Learning(training_data);
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test_error = Bayes_Testing(testing_data, p1, p2, pc1, pc2); % use parameters to calculate test error
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%Part 3: using validataion set to do Bayes_Learning
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[p1,p2,pc1,pc2] = Bayes_Learning(training_data, validation_data); % get p1, p2, pc1, pc2
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[pc1,pc2] %show the best prior
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test_error = Bayes_Testing(testing_data, p1, p2, pc1, pc2); % use parameters to calculate test error
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% by calling Bayes_Learning and Bayes_Testing, the error table of
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% validataion data and test data error is automatically printed to command
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% window
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25
assignments/hwk01/MLE_Learning.m
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assignments/hwk01/MLE_Learning.m
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% implements MLE_Learning, returns the outputs (p1: learned Bernoulli
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% parameters of the first class, p2: learned Bernoulli parameters of the
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% second class; pc1: prior of the first class, pc2: prior of the
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% second class
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function [p1,p2,pc1,pc2] = MLE_Learning(training_data)
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[train_row_size, column_size] = size(training_data); % dimension of training data
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X = training_data(1:train_row_size, 1:column_size-1); %Training data
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y = training_data(:,column_size); % training labels
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% (1) TODO: find label counts of class 1 and class 2
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class1_rows = find(y == 1);
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class1_count = length(class1_rows);
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class2_rows = find(y == 2);
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class2_count = length(class2_rows);
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% (2) TODO: compute priors pc1, pc2
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pc1 = class1_count / train_row_size;
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pc2 = class2_count / train_row_size;
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% (3) TODO: compute maximum likelihood estimate (MLE) p1, p2
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p1 = sum(X(class1_rows, :)) / class1_count;
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p2 = sum(X(class2_rows, :)) / class2_count;
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2
assignments/hwk01/Makefile
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assignments/hwk01/Makefile
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watch:
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watchexec -e md -- pandoc HW1.md -o HW1.pdf
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14
assignments/hwk01/hw1solve.py
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assignments/hwk01/hw1solve.py
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from sympy.abc import i, k, m, n, x
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import sympy
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def prob_2a():
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# f = sympy.Function('f')
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def f(x, theta): return (1.0 / theta) * sympy.exp(- x / theta)
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log = sympy.Sum(f, (k, 1, n))
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print(log)
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prob_2a()
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200
assignments/hwk01/testing_data.txt
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assignments/hwk01/testing_data.txt
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1 0 0 0 0 0 0 0 1 1 1
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0 0 0 0 1 0 0 0 0 0 1
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1 1 0 1 1 0 1 1 1 1 1
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1 1 0 0 0 0 1 0 0 0 1
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0 1 0 0 1 0 1 0 1 0 1
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1 1 0 0 1 0 0 1 1 0 1
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1 1 0 0 1 0 0 0 0 0 1
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1 0 0 0 0 0 0 1 0 0 1
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1 0 0 0 0 0 1 0 1 0 1
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0 0 0 0 0 0 1 0 0 0 1
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1 1 0 0 0 0 0 0 1 0 1
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1 1 0 0 0 0 1 0 0 0 1
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1 1 0 0 0 0 0 0 0 0 1
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1 0 0 0 0 0 0 0 1 0 1
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1 1 0 0 1 0 0 0 0 1 1
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1 0 0 0 1 0 1 0 0 1 1
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1 1 0 0 0 0 0 0 0 1 1
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0 1 0 0 0 0 0 1 1 1 1
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1 0 0 0 0 0 1 0 1 1 1
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1 0 0 0 0 0 1 0 0 0 1
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1 1 0 0 0 0 1 0 1 0 1
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1 1 0 0 1 0 1 0 1 0 1
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0 0 0 0 1 0 0 0 0 0 1
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1 1 0 0 0 0 0 0 1 0 1
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0 1 0 0 1 0 0 1 1 1 1
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1 1 0 0 1 0 1 0 1 0 1
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1 0 0 0 0 0 0 0 0 0 1
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1 1 0 0 0 0 0 0 1 0 1
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1 1 0 0 0 0 1 0 1 0 1
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0 1 0 0 1 0 1 0 0 1 1
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0 1 0 0 1 0 0 0 1 0 1
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0 1 0 0 1 0 0 0 1 0 1
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1 0 0 0 0 0 0 1 1 1 1
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1 1 0 1 1 0 0 0 0 0 1
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1 1 0 0 1 0 0 0 1 0 1
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0 0 0 0 0 0 0 0 0 0 1
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1 1 0 0 0 0 0 0 1 0 1
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1 1 0 0 0 0 0 0 0 1 1
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0 1 0 0 1 0 1 0 0 0 1
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1 0 0 1 1 0 0 0 1 0 1
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1 1 0 0 1 0 0 0 1 0 1
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1 1 0 0 0 0 0 0 0 1 1
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0 0 1 0 0 0 0 0 1 0 1
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0 0 0 0 0 0 0 1 1 1 1
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0 1 0 0 0 0 0 0 1 1 1
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1 1 0 0 1 0 0 0 0 0 1
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1 1 0 0 0 0 0 0 1 0 1
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1 0 0 0 0 0 0 0 1 0 1
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1 1 0 0 1 0 0 0 0 0 1
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1 1 0 0 1 0 0 1 0 1 1
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0 1 0 0 1 0 1 1 1 1 2
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0 0 1 0 0 0 1 1 1 1 2
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1 0 0 0 0 0 1 1 1 1 2
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1 0 0 0 1 0 1 0 1 1 2
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1 0 1 0 1 0 1 1 1 1 2
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0 1 1 0 0 0 0 0 1 1 2
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0 1 0 0 0 0 1 0 1 1 2
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1 1 0 0 1 1 1 0 1 0 2
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1 0 0 0 0 0 1 0 1 0 2
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1 1 0 0 0 0 1 1 1 1 2
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0 0 1 0 0 0 1 1 1 1 2
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1 0 0 0 1 0 1 0 1 1 2
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1 1 0 0 1 0 1 1 1 1 2
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1 1 0 0 0 0 1 1 1 1 2
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1 1 0 0 1 0 1 0 1 1 2
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1 1 1 0 0 0 1 1 1 1 2
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0 1 0 0 0 0 1 0 1 0 2
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0 1 0 0 1 0 1 0 1 1 2
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0 1 0 0 0 0 1 0 1 1 2
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1 1 1 0 1 0 1 0 1 1 2
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0 0 0 0 0 0 1 0 1 1 2
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1 0 0 0 0 0 1 0 1 1 2
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0 0 0 0 1 1 0 1 1 1 2
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1 1 1 0 0 0 1 0 1 0 2
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1 1 0 0 1 0 1 1 1 1 2
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1 1 0 0 0 1 1 0 1 1 2
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1 1 0 0 0 0 1 0 1 1 2
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0 1 0 0 1 0 0 0 1 1 2
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1 1 0 0 0 0 0 0 1 1 2
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0 1 0 0 0 0 1 0 1 1 2
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1 1 0 0 1 0 0 0 1 1 2
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1 1 0 0 0 1 1 0 1 1 2
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0 1 0 0 0 0 1 0 1 1 2
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0 1 0 0 0 1 1 0 1 0 2
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1 1 0 0 1 1 1 0 1 1 2
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0 1 0 0 0 1 1 0 1 1 2
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0 1 0 0 0 0 1 1 1 1 2
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0 0 0 0 1 0 1 0 1 1 2
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1 1 0 0 0 0 1 0 1 1 2
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1 1 0 0 0 0 1 0 1 1 2
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0 1 0 0 1 0 1 1 1 1 2
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1 1 0 0 1 0 0 0 1 1 2
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1 0 0 0 1 0 0 1 1 0 2
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1 1 1 0 1 1 1 1 1 1 2
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0 1 0 0 1 0 0 1 1 1 2
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0 0 0 0 0 0 1 0 1 1 2
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1 1 0 0 0 0 1 1 1 1 2
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0 1 0 0 0 0 1 1 1 1 2
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1 1 0 0 0 0 1 0 1 1 2
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1 1 0 0 0 1 0 1 1 1 2
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0 1 0 0 0 0 1 1 1 1 2
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0 1 0 0 0 0 1 0 1 1 2
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1 0 1 0 0 0 1 0 1 1 2
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0 1 0 0 1 0 1 0 1 1 2
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1 1 1 0 1 0 1 0 1 1 2
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0 1 0 0 1 0 1 0 1 0 2
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1 1 0 0 0 1 1 0 1 1 2
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1 1 0 0 0 0 1 0 1 1 2
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1 1 1 0 0 0 1 0 1 1 2
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0 1 0 0 0 0 1 0 1 1 2
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1 0 0 0 0 1 0 1 1 1 2
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0 0 0 0 0 0 0 0 1 1 2
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0 1 0 0 0 1 1 1 1 0 2
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1 1 0 0 0 0 1 0 1 0 2
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1 1 0 0 1 0 1 0 1 1 2
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0 1 0 0 1 0 1 0 1 0 2
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0 1 0 0 1 1 1 0 1 0 2
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0 1 0 1 0 0 1 1 1 1 2
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0 1 0 0 1 1 1 0 1 1 2
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1 1 0 0 0 0 1 1 0 1 2
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0 0 0 0 0 0 1 0 1 1 2
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0 1 0 0 0 0 1 0 1 1 2
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0 1 0 0 0 0 1 0 1 1 2
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0 1 0 0 1 0 1 0 1 1 2
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0 0 0 0 1 1 1 1 1 0 2
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1 1 0 0 0 0 1 0 1 0 2
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1 0 0 0 1 0 1 1 1 1 2
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1 1 0 0 0 0 1 0 1 1 2
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1 1 0 0 0 1 1 0 1 0 2
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0 1 0 1 0 0 0 0 1 1 2
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1 1 0 0 0 1 1 0 1 1 2
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1 1 0 0 0 1 1 0 1 1 2
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0 1 0 1 0 0 1 0 1 1 2
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1 1 0 0 0 0 1 0 1 0 2
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0 1 1 0 0 0 1 0 1 1 2
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1 0 0 0 1 0 1 0 1 1 2
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1 1 0 0 0 0 1 0 1 0 2
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1 1 0 0 0 1 1 0 1 1 2
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1 1 0 0 1 0 1 0 1 1 2
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0 0 0 0 0 0 1 0 1 1 2
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1 1 0 0 0 1 1 0 1 1 2
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1 1 0 0 0 0 1 0 1 1 2
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1 0 0 0 1 1 1 0 1 1 2
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1 1 0 0 0 1 1 0 1 1 2
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1 1 0 0 1 0 1 0 1 1 2
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1 1 0 0 0 0 1 0 1 1 2
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1 1 0 1 0 0 1 0 1 1 2
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1 1 0 0 1 1 1 1 1 1 2
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0 0 0 0 1 1 1 0 1 1 2
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1 1 0 1 1 1 1 1 1 0 2
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0 1 0 0 1 0 1 0 1 0 2
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0 1 1 0 1 1 1 1 1 1 2
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0 1 0 0 0 0 1 0 1 1 2
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0 1 0 0 0 0 1 0 1 1 2
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1 1 0 0 0 0 1 0 0 1 2
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0 1 0 0 0 0 1 0 1 1 2
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1 0 0 1 0 0 1 0 1 1 2
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1 1 0 0 0 0 1 1 1 1 2
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1 1 0 0 0 0 1 0 1 0 2
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1 1 0 0 0 1 0 0 1 0 2
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0 1 0 0 0 0 1 1 1 1 2
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0 1 0 0 0 1 1 0 1 1 2
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0 1 0 0 1 0 1 1 1 1 2
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1 0 0 0 0 0 1 1 1 1 2
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0 1 0 0 1 1 1 1 1 1 2
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1 1 0 0 0 1 1 0 1 0 2
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1 0 0 0 1 0 1 0 1 0 2
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0 1 0 0 1 1 0 0 0 1 2
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1 0 0 0 1 0 1 0 1 1 2
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1 1 0 0 0 1 1 0 1 1 2
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0 1 0 0 0 0 1 0 1 0 2
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1 1 0 0 0 1 1 0 1 1 2
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1 1 0 0 0 0 1 0 1 0 2
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0 0 0 0 0 1 1 1 1 1 2
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0 1 0 0 1 0 1 1 1 1 2
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0 1 0 0 1 0 1 0 1 1 2
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1 1 0 0 1 0 1 1 1 1 2
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1 1 0 0 0 0 1 0 1 0 2
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0 1 0 0 0 0 1 1 1 0 2
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1 1 0 0 1 0 1 0 1 1 2
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1 1 0 0 1 0 1 0 1 0 2
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0 1 0 0 0 0 1 0 1 1 2
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1 1 0 0 0 1 1 0 1 1 2
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1 1 0 0 1 0 1 0 1 1 2
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0 1 0 0 0 1 1 0 1 1 2
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0 0 0 0 0 0 1 0 1 1 2
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1 1 0 0 0 0 1 0 1 1 2
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0 1 0 0 0 1 1 0 1 0 2
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1 0 0 0 1 0 1 1 1 1 2
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0 0 0 0 0 0 1 0 1 1 2
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1 1 0 0 0 0 1 0 1 1 2
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1 1 1 0 0 0 1 0 1 1 2
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1 1 0 0 1 1 1 0 1 0 2
|
||||
1 1 0 0 0 0 1 0 1 1 2
|
||||
0 1 0 0 1 0 1 0 1 1 2
|
||||
0 1 0 0 0 1 1 0 1 1 2
|
||||
1 1 1 0 0 1 1 0 1 0 2
|
||||
1 1 0 0 0 0 1 1 1 1 2
|
||||
1 1 0 0 0 0 0 0 1 1 2
|
1000
assignments/hwk01/training_data.txt
Normal file
1000
assignments/hwk01/training_data.txt
Normal file
File diff suppressed because it is too large
Load diff
200
assignments/hwk01/validation_data.txt
Normal file
200
assignments/hwk01/validation_data.txt
Normal file
|
@ -0,0 +1,200 @@
|
|||
0 0 0 1 1 0 1 0 0 0 1
|
||||
1 0 0 0 1 0 0 0 1 1 1
|
||||
0 0 0 0 1 0 0 0 1 1 1
|
||||
1 0 0 0 0 0 0 0 1 1 1
|
||||
1 0 0 0 0 0 0 1 0 1 1
|
||||
1 1 0 0 1 0 0 1 0 0 1
|
||||
1 1 1 0 1 0 0 0 1 1 1
|
||||
0 1 0 0 1 0 1 0 0 0 1
|
||||
1 0 0 0 0 0 1 0 1 0 1
|
||||
1 1 0 0 0 0 0 0 1 0 1
|
||||
0 0 0 0 0 0 0 0 0 1 1
|
||||
1 0 0 0 1 0 0 0 0 1 1
|
||||
1 1 0 1 0 0 0 0 0 0 1
|
||||
1 1 0 0 1 0 0 0 1 1 1
|
||||
0 1 0 1 1 0 0 0 1 1 1
|
||||
1 0 0 0 1 0 0 0 1 0 1
|
||||
1 1 0 0 1 0 0 0 0 0 1
|
||||
1 1 0 0 1 0 0 0 0 0 1
|
||||
1 1 0 0 1 0 1 0 1 1 1
|
||||
1 1 0 1 1 0 1 0 1 0 1
|
||||
1 1 0 0 0 0 0 0 0 1 1
|
||||
0 0 0 0 0 0 0 0 0 1 1
|
||||
1 1 0 0 1 0 0 0 0 0 1
|
||||
1 1 0 0 0 0 0 0 1 0 1
|
||||
1 1 0 0 1 0 0 0 0 1 1
|
||||
1 1 0 0 0 0 0 0 1 1 1
|
||||
0 1 0 0 1 0 0 0 0 0 1
|
||||
0 1 1 0 1 0 0 0 1 0 1
|
||||
1 0 0 0 1 1 0 0 1 1 1
|
||||
0 1 0 0 1 0 0 0 0 0 1
|
||||
1 0 0 0 1 0 0 0 0 0 1
|
||||
1 1 0 0 1 0 0 0 1 1 1
|
||||
1 1 0 0 1 0 0 0 1 0 1
|
||||
1 1 0 0 0 0 0 1 1 1 1
|
||||
1 0 0 0 1 0 0 0 1 0 1
|
||||
1 1 0 0 0 0 1 0 1 1 1
|
||||
1 0 0 0 0 0 0 0 1 1 1
|
||||
1 1 0 0 0 0 0 0 1 0 1
|
||||
1 1 0 0 1 0 0 0 0 0 1
|
||||
1 0 0 0 0 0 1 0 1 1 1
|
||||
1 0 0 1 0 0 0 0 1 1 1
|
||||
1 1 0 0 0 0 0 0 0 0 1
|
||||
1 1 0 0 0 0 0 0 0 0 1
|
||||
1 1 0 0 0 0 1 0 1 1 1
|
||||
1 0 0 0 1 0 0 0 0 0 1
|
||||
0 1 0 0 0 0 0 0 1 1 1
|
||||
1 1 0 0 0 0 0 0 1 0 1
|
||||
0 1 1 0 0 0 0 0 1 0 1
|
||||
0 0 0 0 0 0 0 0 0 0 1
|
||||
0 1 0 0 1 0 0 0 1 0 1
|
||||
0 1 0 0 0 0 1 1 1 1 2
|
||||
0 1 0 0 1 1 1 0 1 1 2
|
||||
1 1 1 0 1 1 1 1 1 1 2
|
||||
1 1 0 0 0 0 0 0 1 1 2
|
||||
1 1 0 0 1 0 1 1 1 1 2
|
||||
0 1 0 0 1 0 1 0 1 1 2
|
||||
0 1 0 0 0 0 1 0 1 1 2
|
||||
1 0 0 0 0 0 0 0 1 0 2
|
||||
1 1 0 0 0 0 1 0 1 1 2
|
||||
0 0 1 0 0 0 1 0 1 1 2
|
||||
1 1 0 0 0 0 1 0 1 1 2
|
||||
1 1 0 0 1 1 0 0 1 1 2
|
||||
1 0 0 0 0 0 1 0 1 1 2
|
||||
1 1 0 0 1 0 1 1 1 1 2
|
||||
0 1 0 0 1 0 1 0 1 1 2
|
||||
1 1 0 0 0 0 1 1 1 1 2
|
||||
1 1 0 0 0 0 1 1 1 1 2
|
||||
1 1 0 0 0 1 0 0 1 1 2
|
||||
1 1 0 0 0 0 0 1 1 1 2
|
||||
0 1 0 0 1 0 1 0 1 1 2
|
||||
0 1 0 0 0 0 1 0 1 1 2
|
||||
0 1 0 0 1 0 1 0 1 0 2
|
||||
1 1 0 0 1 0 1 0 1 0 2
|
||||
1 1 0 0 0 1 1 0 1 1 2
|
||||
1 1 0 1 1 0 1 0 1 1 2
|
||||
1 1 0 0 1 0 1 1 1 1 2
|
||||
0 1 0 0 0 0 1 0 1 1 2
|
||||
0 1 0 0 1 0 1 0 1 0 2
|
||||
0 0 0 0 0 1 1 0 1 0 2
|
||||
1 0 0 0 0 1 0 0 1 1 2
|
||||
1 0 1 0 1 0 1 0 1 1 2
|
||||
0 1 0 1 1 1 0 0 1 0 2
|
||||
1 1 0 0 0 1 1 1 1 1 2
|
||||
0 1 0 0 0 1 1 1 1 1 2
|
||||
0 1 0 1 0 0 0 0 1 1 2
|
||||
1 1 0 0 1 0 1 1 1 1 2
|
||||
0 1 0 1 1 1 1 1 1 1 2
|
||||
0 0 0 0 0 1 1 0 1 1 2
|
||||
1 1 0 0 1 0 1 0 1 0 2
|
||||
1 0 0 0 1 0 1 1 1 1 2
|
||||
0 1 0 0 0 1 1 0 1 0 2
|
||||
0 0 0 0 0 0 1 0 1 1 2
|
||||
0 1 0 0 1 1 1 0 1 1 2
|
||||
0 1 1 0 0 1 1 1 1 1 2
|
||||
1 1 0 0 0 1 1 0 1 1 2
|
||||
1 1 0 0 0 1 1 0 1 1 2
|
||||
0 0 0 0 1 1 1 1 1 1 2
|
||||
0 1 1 0 0 0 1 0 1 1 2
|
||||
0 1 0 0 1 1 1 1 1 1 2
|
||||
1 0 0 0 0 0 1 0 1 1 2
|
||||
0 1 0 0 0 0 1 0 1 0 2
|
||||
1 1 0 0 0 0 1 0 1 1 2
|
||||
0 1 0 0 1 1 1 1 1 1 2
|
||||
1 1 0 0 1 0 1 0 1 0 2
|
||||
0 1 0 0 0 1 1 0 1 1 2
|
||||
1 0 0 0 1 0 1 1 1 1 2
|
||||
1 1 1 0 0 0 1 0 1 0 2
|
||||
0 0 0 0 1 0 0 1 1 1 2
|
||||
0 0 0 0 1 1 1 0 1 0 2
|
||||
0 1 0 0 0 0 1 0 1 1 2
|
||||
0 1 0 0 0 0 0 0 1 0 2
|
||||
1 1 0 0 1 0 0 0 1 1 2
|
||||
0 0 0 0 0 1 1 1 1 0 2
|
||||
0 1 0 0 0 0 1 0 1 1 2
|
||||
1 1 0 0 0 0 1 0 1 0 2
|
||||
0 1 0 0 0 0 1 0 1 1 2
|
||||
1 1 0 0 1 0 1 0 1 1 2
|
||||
0 1 0 0 0 1 1 1 1 1 2
|
||||
1 1 1 0 0 1 1 0 1 1 2
|
||||
1 1 1 0 0 0 1 0 1 1 2
|
||||
0 1 0 0 0 0 1 0 1 1 2
|
||||
1 1 0 0 1 1 1 0 1 1 2
|
||||
0 1 1 0 0 1 1 1 1 1 2
|
||||
1 1 0 0 0 0 0 1 1 0 2
|
||||
0 1 0 0 0 0 1 0 1 0 2
|
||||
1 1 0 0 1 0 1 1 1 1 2
|
||||
1 1 0 0 0 1 1 1 1 1 2
|
||||
1 1 0 0 1 1 1 0 0 1 2
|
||||
1 1 0 0 0 0 1 0 1 0 2
|
||||
0 1 0 0 0 0 1 0 1 1 2
|
||||
1 1 0 0 0 1 1 0 1 1 2
|
||||
0 1 0 0 1 1 1 0 1 1 2
|
||||
1 1 0 0 0 1 0 0 1 0 2
|
||||
1 1 0 0 1 1 1 1 1 1 2
|
||||
1 1 0 0 0 1 1 0 1 1 2
|
||||
1 1 0 0 0 1 1 0 1 1 2
|
||||
1 1 0 0 0 0 1 0 1 0 2
|
||||
0 1 0 0 0 0 0 1 1 1 2
|
||||
1 1 0 0 0 0 1 1 1 1 2
|
||||
0 0 0 0 1 0 1 1 1 1 2
|
||||
1 0 1 0 0 0 0 0 1 1 2
|
||||
0 1 0 0 0 0 1 0 1 1 2
|
||||
1 0 0 0 0 1 1 1 1 0 2
|
||||
0 1 0 0 0 0 1 0 1 1 2
|
||||
1 1 0 0 0 1 1 0 1 0 2
|
||||
1 1 1 0 0 1 1 1 1 1 2
|
||||
0 1 0 0 0 1 1 0 1 1 2
|
||||
1 0 0 0 1 0 1 1 1 1 2
|
||||
1 1 0 0 0 1 0 0 1 1 2
|
||||
0 1 0 1 1 0 1 0 1 1 2
|
||||
0 1 0 0 1 0 1 0 1 0 2
|
||||
0 1 0 0 1 1 1 1 1 0 2
|
||||
1 0 0 0 1 1 1 0 1 1 2
|
||||
0 1 0 0 1 0 1 1 1 1 2
|
||||
1 1 0 0 1 0 1 1 1 1 2
|
||||
0 0 0 0 0 0 0 0 1 1 2
|
||||
1 0 0 1 1 0 0 1 1 1 2
|
||||
0 0 0 0 1 0 1 0 1 1 2
|
||||
0 1 0 0 1 1 1 1 1 1 2
|
||||
1 1 0 0 1 1 1 0 1 1 2
|
||||
0 1 0 0 0 1 1 0 1 1 2
|
||||
0 1 0 0 0 0 1 0 1 0 2
|
||||
1 1 0 0 0 0 1 0 1 1 2
|
||||
0 1 0 0 1 1 1 0 1 1 2
|
||||
0 1 0 0 0 1 0 0 1 1 2
|
||||
0 1 0 0 0 1 1 0 1 1 2
|
||||
0 1 0 0 1 1 1 1 1 0 2
|
||||
0 1 1 0 0 0 1 0 1 1 2
|
||||
0 0 0 0 0 0 1 1 1 1 2
|
||||
1 1 0 0 1 0 0 0 1 0 2
|
||||
1 1 0 0 0 1 1 1 1 1 2
|
||||
1 1 0 0 1 0 0 0 1 1 2
|
||||
0 1 0 0 0 1 1 1 1 1 2
|
||||
0 1 0 1 1 0 1 1 1 1 2
|
||||
0 1 1 0 0 1 1 0 1 1 2
|
||||
1 1 0 0 1 0 1 1 1 1 2
|
||||
0 1 0 0 0 1 0 0 1 1 2
|
||||
1 1 0 0 0 0 1 0 1 1 2
|
||||
1 1 1 0 0 0 0 0 1 1 2
|
||||
1 1 0 0 0 1 0 0 1 1 2
|
||||
0 1 0 0 0 1 1 0 1 1 2
|
||||
1 1 0 0 0 0 1 0 1 1 2
|
||||
0 1 0 0 0 0 0 0 1 1 2
|
||||
0 1 0 0 0 0 1 0 1 1 2
|
||||
0 1 0 1 0 0 1 0 1 0 2
|
||||
1 1 1 0 0 0 1 0 1 1 2
|
||||
0 1 0 0 0 1 1 0 1 1 2
|
||||
1 1 1 0 0 0 1 0 1 1 2
|
||||
0 0 0 0 1 0 1 1 1 1 2
|
||||
1 1 0 0 0 1 1 0 1 0 2
|
||||
0 1 0 0 1 0 0 0 1 1 2
|
||||
1 0 0 0 0 1 1 0 1 0 2
|
||||
1 1 0 0 1 1 1 0 1 0 2
|
||||
1 1 0 0 1 0 1 0 1 1 2
|
||||
0 1 0 0 0 1 1 0 1 1 2
|
||||
0 1 0 0 1 0 1 1 1 0 2
|
||||
0 0 0 0 1 0 1 0 1 1 2
|
||||
1 1 0 0 1 0 1 0 1 1 2
|
||||
0 1 0 0 1 1 1 0 1 1 2
|
||||
1 1 0 0 0 0 1 1 1 0 2
|
|
@ -35,8 +35,12 @@ while err > 0
|
|||
%%% ylim([mny mxy]);
|
||||
%ginput(1);
|
||||
% pause(0.5); %change the delay
|
||||
|
||||
rate = rate * 0.9;
|
||||
end
|
||||
end
|
||||
round = round + 1
|
||||
err = sum(sign(w'*X)~=Y')/N %show misclassification rate
|
||||
end
|
||||
round = round + 1;
|
||||
err = sum(sign(w'*X)~=Y')/N; %show misclassification rate
|
||||
end
|
||||
|
||||
round
|
|
@ -2,22 +2,26 @@
|
|||
% Rui Kuang
|
||||
% Run perceptron on random data points in two classes
|
||||
|
||||
n = 20; %set the number of data points
|
||||
mydata = rand(n,2);
|
||||
|
||||
shiftidx = abs(mydata(:,1)-mydata(:,2))>0.05;
|
||||
mydata = mydata(shiftidx,:);
|
||||
myclasses = mydata(:,1)>mydata(:,2); % labels
|
||||
n = size(mydata,1);
|
||||
X = [mydata ones(1,n)']'; Y=myclasses;
|
||||
Y = Y * 2 -1;
|
||||
|
||||
% init weigth vector
|
||||
w = [mean(mydata) 0]';
|
||||
% n = 200; %set the number of data points
|
||||
% mydata = rand(n,2);
|
||||
%
|
||||
% shiftidx = abs(mydata(:,1)-mydata(:,2))>0.00005;
|
||||
% mydata = mydata(shiftidx,:);
|
||||
% myclasses = mydata(:,1)>mydata(:,2); % labels
|
||||
% n = size(mydata,1);
|
||||
% X = [mydata ones(1,n)']'; Y=myclasses;
|
||||
% Y = Y * 2 -1;
|
||||
%
|
||||
% % init weigth vector
|
||||
% %%% w = [mean(mydata) 0]';
|
||||
% w = [1 0 0];
|
||||
|
||||
for i = 1:1
|
||||
w=rand(1,3)';
|
||||
w(3,1)=0;%go through the origin for visualization
|
||||
%%% w=rand(1,3)'
|
||||
w = [0.6842
|
||||
0.5148
|
||||
0]
|
||||
w(3,1)=0%go through the origin for visualization
|
||||
% call perceptron
|
||||
wtag=perceptron(X,Y,w,10);
|
||||
end
|
||||
|
|
1
zoo/.gitignore
vendored
Normal file
1
zoo/.gitignore
vendored
Normal file
|
@ -0,0 +1 @@
|
|||
trees
|
Loading…
Reference in a new issue