Testing to main

.gitignore

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+package.json
+package-lock.json
+build/
+plugins/
+node_modules/
+.DS_Store

.vscode/settings.json

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+{
+    "liveServer.settings.port": 5501
+}

experiment-descriptor.json

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   "unit-type": "lu",
   "label": "",
   "basedir": ".",
+  "LaTeXinMD": "true",
   "units": [
     {
       "unit-type": "aim"

experiment/aim.md

@@ -1,5 +1,28 @@
+### Understanding Classification Approaches
 
+#### Discriminative vs Generative Classifiers
+1. **Discriminative Classifiers** (e.g., Linear Perceptrons)
+   - Learn decision boundaries directly
+   - Effective for well-separated classes
+   - Focus on class separation
+   - Simpler to implement
 
-Linear perceptrons allow us to learn a decision boundary that would separate two classes. They are very effective when there are only two classes, and they are well separated. Such classifiers are referred to as discriminative classifiers.
+2. **Generative Classifiers** (Bayesian Approach)
+   - Model each class as a random vector
+   - Use probability distributions/density functions
+   - Consider class likelihoods
+   - More flexible in handling complex data
 
-In contrast, generative classifiers consider each sample as a random vector, and explicity model each class by their distribution or density functions. To carry out the classification, we compute the likelihood that a given sample belong to each of the candidate classes, and assign the sample to the class that is most likely. In other words, we need to compute P(ωi/x) for each class ωi. However, the density functions provide only the likelihood of seeing a particular sample, given that the sample belongs to a specific class. i.e., the density functions provide us p(x/ωi). The Bayes rule provides us with an approach to compute the likelihood of the class for a given sample, from the density functions and related information.
+### Bayesian Classification
+The experiment focuses on understanding how Bayesian classification:
+- Computes class probabilities using Bayes' rule
+- Combines prior knowledge with observed evidence
+- Makes decisions based on posterior probabilities
+- Handles uncertainty in classification
+
+### Key Concepts
+- Prior probabilities
+- Likelihood functions
+- Posterior probabilities
+- Decision boundaries
+- Class density functions

experiment/assignment.md

@@ -1,14 +1,34 @@
- 1. The covariance matrix is always
+### Question 1: Covariance Matrix Properties
+The covariance matrix is always:
 
-    a) Square
+a) Square  
+b) Positive Semidefinite  
+c) Positive Definite  
+d) Symmetric  
+e) None of the above
 
-    b) Positive Semidefinite
+For each of the properties you selected, describe what would happen if the covariance matrix does not satisfy that property.
 
-    c) Positive Definite
+### Question 2: Decision Boundaries
+Describe the possible set of decision boundaries that can be generated using Gaussian density functions in a two-class problem.
 
-    d) Symmetric
+### Question 3: Bayesian Classification Components
+Consider a two-class classification problem using Bayesian approach:
 
-    e) None of the above.
-    For each of the properties you selected, describe what would happen if the covariance matrix does not satisfy that property.
+a) Explain how the prior probabilities affect the decision boundary when:
+   - Both classes have equal priors
+   - One class has a higher prior than the other
 
-2. Describe the possible set of decision boundaries that can be generated using gaussian density functions in a two class problem. 
+b) How does the likelihood function influence the classification decision when:
+   - The classes have similar covariance matrices
+   - The classes have different covariance matrices
+
+### Question 4: Practical Application
+Given a dataset with two classes, where:
+- Class 1: Mean = (2, 3), Covariance = [[1, 0], [0, 1]]
+- Class 2: Mean = (4, 5), Covariance = [[2, 0], [0, 2]]
+- Prior probabilities: P(Class 1) = 0.6, P(Class 2) = 0.4
+
+a) Calculate the posterior probability for a test point (3, 4)
+b) Determine the class assignment for this point
+c) Explain how the decision would change if the prior probabilities were equal 

experiment/objective.md

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 The high level goals of the experiment are:
 
  1. To understand the computation of likelihood of a class, given a sample.
-
  2. To understand the the use of density/distribution functions to model a class.
  3. To understand the effect of prior probabilities in Bayesian classification.
-
  4. To understand how two (or more) density functions interact in the feature space to decide a decision boundary between classes.
-
  5. To understand how the decision boundary varies based on nature of the density functions.
 

experiment/procedure.md

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-**Stage 1:**
+### Set Class Parameters
+In the **Parameters** section on the left side:
 
- 1. Lauch the experiment and clear the pane. Assign differnt means and covariances for each of the classes and observe the resulting densities. Use the mark-all button to observe the decision boundaries.
+   * You will see parameters for **Class 1** and **Class 2**.
+   * Each class has fields to set:
 
- 2. Note down your observations on the relationship between the decision boundaries and the density functions.
+     * Mean values (`Mean X`, `Mean Y`)
+     * Covariance matrix values (`Covariance XX`, `Covariance XY`, `Covariance YY`)
+     * Prior Probability (the probability of each class before seeing data)
 
-**Stage 2:**
+### Adjust Parameters as Desired
 
- 1. Repeat the above procedure for different values of prior probabilities.
+* Modify any of the values by clicking on the input box and typing new numbers.
+* The means define the center location of each class’s distribution.
+* The covariance matrix controls the shape and orientation of the distribution.
+* The prior probability defines how likely each class is, used for classification decisions.
 
- 2. Observe the change in the scaled density functions and decision boundaries
+### Visualize Classification
 
- 3. Note down your observations regarding the change of decision boundaries
+After setting the parameters, the visualization will automatically reflect these changes on the plot canvas (600x600 pixels) on the right.
 
-**Stage 3:**
+#### Use Buttons to Interact with the Plot
 
-Generate the following types of decision boundaries by varying the means and covariance matrices.
+* **Mark All:** Click the **Mark All** button to classify and mark all points in the visualization area based on the current parameters.
+* **Clear:** Click the **Clear** button to remove all marks and reset the plot display.
 
-  1. Straight line
-
-  2. Parallel Straight lines
-
-  3. Concentric circles
-  4. Parabola
-  5. Hyperbola
-  6. Four Quadrants
-
-Explain why these shapes are generated in each case.
+#### Interpret the Visualization
+* The plot shows the 2D distributions for Class 1 and Class 2 based on your parameter inputs.
+* Points or areas marked correspond to the classification regions according to Bayesian decision rules.

experiment/simulation/README.md

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+the files used for simulation are:
+- index.html
+- script.js
+- style.css
+
+rest of the files are NOT USED for this version

experiment/simulation/index.html

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 <!DOCTYPE html>
-<html>
+<html lang="en">
 <head>
-	<!-- Add CSS at the head of HTML file -->
-    <link rel="stylesheet" href="./css/main.css">
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0, maximum-scale=1.0, user-scalable=no">
+    <meta name="description" content="Interactive Bayesian Classification Simulation">
+    <meta name="theme-color" content="#3498db">
+    <title>Bayesian Classification Simulation</title>
+    <link rel="stylesheet" href="style.css">
 </head>
 <body>
-		<!-- Your code goes here-->
-	<p><li>This simulation has some <a href="../../../Introduction.html" target="PARENT">software dependencies</a>. We highly recommend you to use our customized <a href="https://vlead.vlabs.ac.in/development/#virtual-box" target="blank">VirtualBox</a> for the smooth running of these simulations.</li></p>
-	<p><li>Click <a href="Exp5.tar.gz">here</a> to open the Experiment for Linux systems</li></p>
-	<p><li>Click <a href="Exp5.zip">here</a> to open the Experiment for Windows</li></p>
+    <div class="container">
+        <header>
+            <h1>Bayesian Classification Simulation</h1>
+        </header>
 
-	<!-- Add JS at the bottom of HTML file --> 
-        <!--<script src="./js/main.js"></script>-->
+        <div class="main-content">
+            <div class="control-panel">
+                <div class="experiment-controls">
+                    <h2>Parameters</h2>
+
+                    <div class="class-params">
+                        <h3>Class 1 Parameters</h3>
+                        <div class="param-group">
+                            <label>Mean X:</label>
+                            <input type="text" id="mean1x" value="-2">
+                        </div>
+                        <div class="param-group">
+                            <label>Mean Y:</label>
+                            <input type="text" id="mean1y" value="-2">
+                        </div>
+                        <div class="param-group">
+                            <label>Covariance XX:</label>
+                            <input type="text" id="cov1xx" value="1">
+                        </div>
+                        <div class="param-group">
+                            <label>Covariance XY:</label>
+                            <input type="text" id="cov1xy" value="0">
+                        </div>
+                        <div class="param-group">
+                            <label>Covariance YY:</label>
+                            <input type="text" id="cov1yy" value="1">
+                        </div>
+                        <div class="param-group">
+                            <label>Prior Probability:</label>
+                            <input type="text" id="prior1" value="0.5">
+                        </div>
+                    </div>
+
+                    <div class="class-params">
+                        <h3>Class 2 Parameters</h3>
+                        <div class="param-group">
+                            <label>Mean X:</label>
+                            <input type="text" id="mean2x" value="2">
+                        </div>
+                        <div class="param-group">
+                            <label>Mean Y:</label>
+                            <input type="text" id="mean2y" value="2">
+                        </div>
+                        <div class="param-group">
+                            <label>Covariance XX:</label>
+                            <input type="text" id="cov2xx" value="1">
+                        </div>
+                        <div class="param-group">
+                            <label>Covariance XY:</label>
+                            <input type="text" id="cov2xy" value="0">
+                        </div>
+                        <div class="param-group">
+                            <label>Covariance YY:</label>
+                            <input type="text" id="cov2yy" value="1">
+                        </div>
+                        <div class="param-group">
+                            <label>Prior Probability:</label>
+                            <input type="text" id="prior2" value="0.5">
+                        </div>
+                    </div>
+
+                    <div class="buttons">
+                        <button style="display: none;" id="generate-btn">Generate Data</button>
+                        <button id="mark-all-btn">Mark All</button>
+                        <button id="clear-btn">Clear</button>
+                    </div>
+                </div>            </div>
+
+            <div class="visualization">
+                <canvas id="plot-canvas" width="600" height="600"></canvas>
+            </div>
+        </div>
+
+
+    </div>
+
+    <script src="script.js"></script>
 </body>
 </html>