From ff1017873072185f78275c12c7d6107b5b89d002 Mon Sep 17 00:00:00 2001
From: rakeshreddy06 <rakeshreddy5566@gmail.com>
Date: Thu, 10 Oct 2024 14:39:29 -0500
Subject: [PATCH 01/31] commit 1

---
 Final_LDA.ipynb      | 308 ++++++++++++++++++
 README.md            |  73 ++++-
 finalLDA.py          | 162 ++++++++++
 generated_data.csv   | 750 +++++++++++++++++++++++++++++++++++++++++++
 requirements.txt     |   5 +-
 testDataGenerator.py |  37 +++
 6 files changed, 1327 insertions(+), 8 deletions(-)
 create mode 100644 Final_LDA.ipynb
 create mode 100644 finalLDA.py
 create mode 100644 generated_data.csv
 create mode 100644 testDataGenerator.py

diff --git a/Final_LDA.ipynb b/Final_LDA.ipynb
new file mode 100644
index 0000000..58446b1
--- /dev/null
+++ b/Final_LDA.ipynb
@@ -0,0 +1,308 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import argparse\n",
+    "from matplotlib import pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class LDAModel:\n",
+    "    def __init__(self, nComponents=2, regularization=0.01, solver=\"eigh\"):\n",
+    "      self.nComponents = nComponents\n",
+    "      self.regularization = regularization\n",
+    "      self.solver = solver\n",
+    "      self.Eigens = None \n",
+    "      self.classes_ = None\n",
+    "      self.classMeans = None\n",
+    "    def fit(self, X, y):\n",
+    "    \n",
+    "      # this will find all the unique classes in the dataset\n",
+    "      self.classes_ = np.unique(y)\n",
+    "      # this will get the number of features in the dataset\n",
+    "      n_features = X.shape[1]\n",
+    "      \n",
+    "      # this will  calculate the scatter and matrices\n",
+    "      WithinScatterMtrx, BetweenScatterMtrx = self.calculate_scatter_matrices(X, y)\n",
+    "      WithinScatterMtrx +=self.regularization * np.identity(n_features)\n",
+    "      if self.solver == \"eigh\":\n",
+    "      # this will calculate the eigens\n",
+    "        eigenValues, eigenVectors = np.linalg.eigh(np.linalg.inv(WithinScatterMtrx).dot(BetweenScatterMtrx))\n",
+    "      else:\n",
+    "        eigenValues, eigenVectors = np.linalg.eig(np.linalg.inv(WithinScatterMtrx).dot(BetweenScatterMtrx))\n",
+    "    \n",
+    "    \n",
+    "      # this will sort the eigens\n",
+    "      \n",
+    "     \n",
+    "      sorted_indices = np.argsort(-eigenValues.real)\n",
+    "      #decending order\n",
+    "      eigenValues = eigenValues.real[sorted_indices]\n",
+    "      eigenVectors = eigenVectors.real[:, sorted_indices]\n",
+    "      \n",
+    "      # this will select the top nComponents eigenvectors\n",
+    "      if self.nComponents is not None:\n",
+    "          eigenVectors = eigenVectors[:, :self.nComponents]\n",
+    "      else:\n",
+    "          self.nComponents = len(self.classes_) - 1\n",
+    "          eigenVectors = eigenVectors[:, :self.nComponents]\n",
+    "      \n",
+    "      self.Eigens = eigenVectors\n",
+    "      \n",
+    "      # this will calculate the class means\n",
+    "      XLDA = X.dot(self.Eigens)\n",
+    "      # this will calculate mean of the class\n",
+    "      self.classMeans = {cls: XLDA[y == cls].mean(axis=0) for cls in self.classes_}\n",
+    "      \n",
+    "      return self\n",
+    "    #this will calculate the scatter matrices\n",
+    "    def calculate_scatter_matrices(self,X_train, y_train):\n",
+    "        speciesData = {label: X_train[y_train == label] for label in np.unique(y_train)}\n",
+    "        dataMeanValues = {label: np.mean(speciesData[label], axis=0) for label in speciesData}\n",
+    "\n",
+    "    \n",
+    "        MeanOverall = np.mean(X_train, axis=0).reshape(-1, 1)\n",
+    "\n",
+    "\n",
+    "        n_features = X_train.shape[1]\n",
+    "        WithinScatterMtrx = np.zeros((n_features, n_features))\n",
+    "        BetweenScatterMtrx = np.zeros((n_features, n_features))\n",
+    "\n",
+    "\n",
+    "        \n",
+    "        for label, class_data in speciesData.items():\n",
+    "            n_i = class_data.shape[0]\n",
+    "            class_mean = dataMeanValues[label].reshape(n_features, 1)\n",
+    "            \n",
+    "        \n",
+    "            for sample in class_data:\n",
+    "                sample = sample.reshape(n_features, 1)\n",
+    "                WithinScatterMtrx += (sample - class_mean).dot((sample - class_mean).T)\n",
+    "            \n",
+    "        \n",
+    "            mean_diff = class_mean - MeanOverall\n",
+    "            BetweenScatterMtrx += n_i * mean_diff.dot(mean_diff.T)\n",
+    "\n",
+    "        return WithinScatterMtrx, BetweenScatterMtrx\n",
+    "    \n",
+    "    def transform(self, X):\n",
+    "        #this will transform the data to the LDA space\n",
+    "        if self.Eigens is None:\n",
+    "            raise ValueError(\"Model has not been fitted yet.\")\n",
+    "        return X.dot(self.Eigens)\n",
+    "    def get_params(self):\n",
+    "        # this will return the parameters of the model\n",
+    "        return {\n",
+    "            'nComponents': self.nComponents,\n",
+    "            'classes_': self.classes_,\n",
+    "            'Eigens': self.Eigens,\n",
+    "            'classMeans': self.classMeans\n",
+    "        }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class RDAModelResults:\n",
+    "  #this is a class to handle the predictions\n",
+    "  def __init__(self, model):\n",
+    "      # this will initialize the model\n",
+    "      self.model = model\n",
+    "\n",
+    "  def predict(self, X):\n",
+    "     # this will transform the data to the LDA space\n",
+    "      XLDA = self.model.transform(X)\n",
+    "      predictions = []\n",
+    "      # this will compute the distances to each class mean\n",
+    "      for sample in XLDA:\n",
+    "          distances = {cls: np.linalg.norm(sample - mean) for cls, mean in self.model.classMeans.items()}\n",
+    "          # this will assign to the class with the nearest mean\n",
+    "          predicted_class = min(distances, key=lambda k: distances[k])\n",
+    "          predictions.append(predicted_class)\n",
+    "      return np.array(predictions)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#this will plot the LDA projection\n",
+    "def plot_lda_projection(XLDA, y, title):\n",
+    "\n",
+    "    plt.figure(figsize=(8, 6))\n",
+    "    for label in np.unique(y):\n",
+    "        plt.scatter(XLDA[y == label, 0], XLDA[y == label, 1], label=f'Class {label}')\n",
+    "    plt.title(title)\n",
+    "    plt.xlabel('LD1')\n",
+    "    if XLDA.shape[1] > 1:\n",
+    "        plt.ylabel('LD2')\n",
+    "    plt.legend()\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def manual_train_test_split(X, y, test_size=0.2, random_state=None):\n",
+    "    if random_state is not None:\n",
+    "        np.random.seed(random_state)\n",
+    "    \n",
+    "    indices = np.random.permutation(len(X))\n",
+    "    test_size = int(len(X) * test_size)\n",
+    "    train_indices = indices[test_size:]\n",
+    "    test_indices = indices[:test_size]\n",
+    "    \n",
+    "    return X[train_indices], X[test_indices], y[train_indices], y[test_indices]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 0.88389311  0.19586502  0.35753652 ...  0.55969629  0.93946935\n",
+      "  -0.97848104]\n",
+      " [ 0.50309684  0.40641447  0.32346101 ... -0.84236793 -1.27950266\n",
+      "   0.24571517]\n",
+      " [-0.0441948   1.56763255  1.05110868 ... -3.18970279  1.12013226\n",
+      "   1.33277821]\n",
+      " ...\n",
+      " [ 8.9530117   6.45568569  9.15746661 ...  7.73010469  7.29285848\n",
+      "   8.33342744]\n",
+      " [ 7.69252773  9.14447362  7.36241476 ...  8.55549506  6.4758956\n",
+      "   7.28877739]\n",
+      " [ 7.67724183  7.35172242  6.5933604  ...  6.95932099  7.39514765\n",
+      "   6.89892766]]\n",
+      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+      " 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
+      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
+      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
+      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
+      " 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
+      " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
+      " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
+      " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
+      " 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3\n",
+      " 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3\n",
+      " 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3\n",
+      " 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3\n",
+      " 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4\n",
+      " 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4\n",
+      " 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4\n",
+      " 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4\n",
+      " 4 4 4 4 4 4 4 4 4 4]\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = pd.read_csv(\"generated_data.csv\", header=None)\n",
+    "\n",
+    "X_features = df.iloc[:, :-1].values #this is the training data\n",
+    "y_labels= df .iloc[:, -1].values #this is the training labels\n",
+    "print(X_features)\n",
+    "print(y_labels)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = manual_train_test_split(X_features, y_labels, test_size=0.2, random_state=42)\n",
+    "model = LDAModel(nComponents=5, regularization=0.05, solver=\"eigen\") \n",
+    "model.fit(X_train, y_train)\n",
+    "\n",
+    "X_test_lda = model.transform(X_test)\n",
+    "\n",
+    "model_results = RDAModelResults(model)\n",
+    "y_pred = model_results.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test Accuracy Evaluation: 100.00%\n"
+     ]
+    }
+   ],
+   "source": [
+    "accuracy = np.mean(y_pred == y_test)\n",
+    "print(f\"Test Accuracy Evaluation: {accuracy * 100:.2f}%\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_lda_projection(X_test_lda, y_test, \"LDA Projection\")\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/README.md b/README.md
index c1e8359..ea0dbce 100644
--- a/README.md
+++ b/README.md
@@ -1,8 +1,69 @@
-# Project 1 
+# README
 
-Put your README here. Answer the following questions.
+## Overview
 
-* What does the model you have implemented do and when should it be used?
-* How did you test your model to determine if it is working reasonably correctly?
-* What parameters have you exposed to users of your implementation in order to tune performance? (Also perhaps provide some basic usage examples.)
-* Are there specific inputs that your implementation has trouble with? Given more time, could you work around these or is it fundamental?
+This project implements a Linear Discriminant Analysis (LDA) model for dimensionality reduction and classification. The model projects high-dimensional data into a lower-dimensional space, maximizing the distance between classes. It is particularly useful for visualizing data with a reduced number of features.
+
+### When to Use the Model
+
+- **Classification Tasks**: Suitable for classifying data into different classes.
+- **Multivariate Normal Distribution Assumption**: Effective when data for each class follows a normal distribution.
+- **Dimensionality Reduction**: Reduces data to lower dimensions while maintaining class separability.
+
+## Running the Program
+
+### Step 1: Generate the Data
+
+To generate synthetic data, use the following command:
+bash
+py .\testDataGenerator.py -N <Sample size> -f <features> -c <classes> -seed 10000 -output_file generated_data.csv
+
+
+Example:
+
+bash
+py .\testDataGenerator.py -N 150 -f 8 -c 5 -seed 10000 -output_file generated_data.csv
+
+
+### Step 2: Run the Program
+
+Execute the final LDA model with:
+
+bash
+python final
+
+
+## Model Testing
+
+### Step 1: Training on Available Dataset (Iris)
+
+- Initially, the model was built using the Iris dataset, a common classification problem involving classifying flowers into one of three different iris species.
+
+### Step 2: Generated Data
+
+- The model was tested using synthetic data generated from `data_generator.py`, allowing for testing under various conditions with variable classes, samples, and features.
+
+### Step 3: Manual Train-Test Split
+
+- Data is split into training and test sets to evaluate performance, preventing overfitting and ensuring accurate training.
+
+### Step 4: Accuracy Calculation
+
+- The model’s predicted classes are compared against the true classes to calculate accuracy.
+
+## User-Exposed Parameters
+
+Users can adjust the following parameters to tune model performance:
+
+- **nComponents**: Number of components to keep in LDA, controlling dimensionality reduction.
+- **Regularization**: Adds regularization to the within-scatter matrix, preventing overfitting in high-dimensional data.
+- **Solver**: Allows choice of eigenvalue computation method, introducing flexibility.
+- **Test Size**: Determines the percentage of data allocated for testing.
+- **Random State**: Ensures reproducibility of train-test splits and generated data.
+
+## Known Limitations
+
+The implementation may encounter difficulties with:
+
+1. **Highly Imbalanced Data**: Struggles when class sizes are noticeably imbalanced.
+2. **High Dimensionality with Few Samples**: May exhibit irregular behavior when the number of features exceeds the number of samples. Dimensionality reduction using PCA can be a solution.
diff --git a/finalLDA.py b/finalLDA.py
new file mode 100644
index 0000000..348a1e4
--- /dev/null
+++ b/finalLDA.py
@@ -0,0 +1,162 @@
+import argparse
+from matplotlib import pyplot as plt
+import numpy as np
+import pandas as pd
+#This is a linear discriminant analysis model
+class LDAModel:
+    def __init__(self, nComponents=2, regularization=0.01, solver="eigh"):
+      self.nComponents = nComponents
+      self.regularization = regularization
+      self.solver = solver
+      self.Eigens = None 
+      self.classes_ = None
+      self.classMeans = None
+    def fit(self, X, y):
+    
+      # this will find all the unique classes in the dataset
+      self.classes_ = np.unique(y)
+      # this will get the number of features in the dataset
+      n_features = X.shape[1]
+      
+      # this will  calculate the scatter and matrices
+      WithinScatterMtrx, BetweenScatterMtrx = self.calculate_scatter_matrices(X, y)
+      WithinScatterMtrx +=self.regularization * np.identity(n_features)
+      if self.solver == "eigh":
+      # this will calculate the eigens
+        eigenValues, eigenVectors = np.linalg.eigh(np.linalg.inv(WithinScatterMtrx).dot(BetweenScatterMtrx))
+      else:
+        eigenValues, eigenVectors = np.linalg.eig(np.linalg.inv(WithinScatterMtrx).dot(BetweenScatterMtrx))
+    
+    
+      # this will sort the eigens
+      
+     
+      sorted_indices = np.argsort(-eigenValues.real)
+      #decending order
+      eigenValues = eigenValues.real[sorted_indices]
+      eigenVectors = eigenVectors.real[:, sorted_indices]
+      
+      # this will select the top nComponents eigenvectors
+      if self.nComponents is not None:
+          eigenVectors = eigenVectors[:, :self.nComponents]
+      else:
+          self.nComponents = len(self.classes_) - 1
+          eigenVectors = eigenVectors[:, :self.nComponents]
+      
+      self.Eigens = eigenVectors
+      
+      # this will calculate the class means
+      XLDA = X.dot(self.Eigens)
+      # this will calculate mean of the class
+      self.classMeans = {cls: XLDA[y == cls].mean(axis=0) for cls in self.classes_}
+      
+      return self
+    #this will calculate the scatter matrices
+    def calculate_scatter_matrices(self,X_train, y_train):
+        speciesData = {label: X_train[y_train == label] for label in np.unique(y_train)}
+        dataMeanValues = {label: np.mean(speciesData[label], axis=0) for label in speciesData}
+
+    
+        MeanOverall = np.mean(X_train, axis=0).reshape(-1, 1)
+
+
+        n_features = X_train.shape[1]
+        WithinScatterMtrx = np.zeros((n_features, n_features))
+        BetweenScatterMtrx = np.zeros((n_features, n_features))
+
+
+        
+        for label, class_data in speciesData.items():
+            n_i = class_data.shape[0]
+            class_mean = dataMeanValues[label].reshape(n_features, 1)
+            
+        
+            for sample in class_data:
+                sample = sample.reshape(n_features, 1)
+                WithinScatterMtrx += (sample - class_mean).dot((sample - class_mean).T)
+            
+        
+            mean_diff = class_mean - MeanOverall
+            BetweenScatterMtrx += n_i * mean_diff.dot(mean_diff.T)
+
+        return WithinScatterMtrx, BetweenScatterMtrx
+    
+    def transform(self, X):
+        #this will transform the data to the LDA space
+        if self.Eigens is None:
+            raise ValueError("Model has not been fitted yet.")
+        return X.dot(self.Eigens)
+    def get_params(self):
+        # this will return the parameters of the model
+        return {
+            'nComponents': self.nComponents,
+            'classes_': self.classes_,
+            'Eigens': self.Eigens,
+            'classMeans': self.classMeans
+        }
+class RDAModelResults:
+  #this is a class to handle the predictions
+  def __init__(self, model):
+      # this will initialize the model
+      self.model = model
+
+  def predict(self, X):
+     # this will transform the data to the LDA space
+      XLDA = self.model.transform(X)
+      predictions = []
+      # this will compute the distances to each class mean
+      for sample in XLDA:
+          distances = {cls: np.linalg.norm(sample - mean) for cls, mean in self.model.classMeans.items()}
+          # this will assign to the class with the nearest mean
+          predicted_class = min(distances, key=lambda k: distances[k])
+          predictions.append(predicted_class)
+      return np.array(predictions)
+
+def plot_lda_projection(XLDA, y, title):
+    # plot it 
+    plt.figure(figsize=(8, 6))
+    for label in np.unique(y):
+        plt.scatter(XLDA[y == label, 0], XLDA[y == label, 1], label=f'Class {label}')
+    plt.title(title)
+    plt.xlabel('LD1')
+    if XLDA.shape[1] > 1:
+        plt.ylabel('LD2')
+    plt.legend()
+    plt.show()
+
+
+
+
+
+def manual_train_test_split(X, y, test_size=0.2, random_state=None):
+    if random_state is not None:
+        np.random.seed(random_state)
+    
+    indices = np.random.permutation(len(X))
+    test_size = int(len(X) * test_size)
+    train_indices = indices[test_size:]
+    test_indices = indices[:test_size]
+    
+    return X[train_indices], X[test_indices], y[train_indices], y[test_indices]
+
+df = pd.read_csv("generated_data.csv", header=None)
+
+X_features = df.iloc[:, :-1].values #this is the training data
+y_labels= df .iloc[:, -1].values #this is the training labels
+
+X_train, X_test, y_train, y_test = manual_train_test_split(X_features, y_labels, test_size=0.2, random_state=42)
+
+
+model = LDAModel(nComponents=5, regularization=0.05, solver="eigen") 
+model.fit(X_train, y_train)
+
+X_test_lda = model.transform(X_test)
+
+model_results = RDAModelResults(model)
+y_pred = model_results.predict(X_test)
+accuracy = np.mean(y_pred == y_test)
+print(f"Test Accuracy: {accuracy * 100:.2f}%")
+
+plot_lda_projection(X_test_lda, y_test, "LDA Projection")
+
+
diff --git a/generated_data.csv b/generated_data.csv
new file mode 100644
index 0000000..04eeea2
--- /dev/null
+++ b/generated_data.csv
@@ -0,0 +1,750 @@
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+7.677241831859459,7.351722417896979,6.593360397893875,7.553905303472652,7.79550169475631,6.9593209873523465,7.395147651318571,6.898927664778979,4
diff --git a/requirements.txt b/requirements.txt
index 18af45d..eba6b88 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,3 +1,4 @@
 numpy
-pytest
-ipython
+matplotlib
+pandas
+
diff --git a/testDataGenerator.py b/testDataGenerator.py
new file mode 100644
index 0000000..ed3f5a7
--- /dev/null
+++ b/testDataGenerator.py
@@ -0,0 +1,37 @@
+import argparse
+import numpy as np  
+
+def generate_random_data(n_samples_per_class=50, n_features=4, n_classes=3, seed=20):
+    np.random.seed(20)
+    X = []
+    y = []
+    for class_label in range(n_classes):
+        class_data = np.random.randn(n_samples_per_class, n_features) + class_label * 2 
+        X.append(class_data)
+        y.extend([class_label] * n_samples_per_class)
+
+    X = np.vstack(X)
+    y = np.array(y)
+    
+    return X, y
+
+def write_data(output_file, X, y):
+    with open(output_file, 'w') as file:
+        for i in range(len(X)):
+            line = ','.join(map(str, X[i])) + ',' + str(y[i]) + '\n'
+            file.write(line)
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("-N", type = int, help="Number of samples")
+    parser.add_argument("-f", type = int, help="Number of features")
+    parser.add_argument("-c", type = int, help="Number of classes")
+    parser.add_argument("-seed", type=int, help="A seed to control randomnes")
+    parser.add_argument("-output_file", type=str, help="Path to output file")
+    args = parser.parse_args()
+    X, y = generate_random_data(args.N, args.f, args.c, args.seed)
+    write_data(args.output_file, X,y)
+
+if __name__=="__main__":
+    main()
+

From 2a5b258786ae6c76ea3cf772eba4d9eef5e59b38 Mon Sep 17 00:00:00 2001
From: Rakesh <40319883+rakeshreddy06@users.noreply.github.com>
Date: Thu, 10 Oct 2024 14:41:18 -0500
Subject: [PATCH 02/31] Update README.md

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index ea0dbce..60e7102 100644
--- a/README.md
+++ b/README.md
@@ -41,7 +41,7 @@ python final
 
 ### Step 2: Generated Data
 
-- The model was tested using synthetic data generated from `data_generator.py`, allowing for testing under various conditions with variable classes, samples, and features.
+- The model was tested using synthetic data generated from `generated_data.py`, allowing for testing under various conditions with variable classes, samples, and features.
 
 ### Step 3: Manual Train-Test Split
 

From 1e5ac73fb08d4221f05004281918fc29e9fc1c90 Mon Sep 17 00:00:00 2001
From: Rakesh <40319883+rakeshreddy06@users.noreply.github.com>
Date: Thu, 10 Oct 2024 14:45:03 -0500
Subject: [PATCH 03/31] Update README.md

---
 README.md | 1 -
 1 file changed, 1 deletion(-)

diff --git a/README.md b/README.md
index 60e7102..5496e63 100644
--- a/README.md
+++ b/README.md
@@ -28,7 +28,6 @@ py .\testDataGenerator.py -N 150 -f 8 -c 5 -seed 10000 -output_file generated_da
 ### Step 2: Run the Program
 
 Execute the final LDA model with:
-
 bash
 python final
 

From a043a7bdbba2b0ad1dfe76baab4c9bfbdc4d3dd8 Mon Sep 17 00:00:00 2001
From: Rakesh <40319883+rakeshreddy06@users.noreply.github.com>
Date: Thu, 10 Oct 2024 14:46:41 -0500
Subject: [PATCH 04/31] Update README.md

---
 README.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/README.md b/README.md
index 5496e63..f99bede 100644
--- a/README.md
+++ b/README.md
@@ -1,5 +1,7 @@
 # README
 
+RakeshReddy - A20525389
+
 ## Overview
 
 This project implements a Linear Discriminant Analysis (LDA) model for dimensionality reduction and classification. The model projects high-dimensional data into a lower-dimensional space, maximizing the distance between classes. It is particularly useful for visualizing data with a reduced number of features.

From 70a2126872f617d0159d6f178e63ce7ac8f27e73 Mon Sep 17 00:00:00 2001
From: Rakesh <40319883+rakeshreddy06@users.noreply.github.com>
Date: Thu, 10 Oct 2024 14:47:23 -0500
Subject: [PATCH 05/31] Update README.md

---
 README.md | 8 ++------
 1 file changed, 2 insertions(+), 6 deletions(-)

diff --git a/README.md b/README.md
index f99bede..923ceb1 100644
--- a/README.md
+++ b/README.md
@@ -17,21 +17,17 @@ This project implements a Linear Discriminant Analysis (LDA) model for dimension
 ### Step 1: Generate the Data
 
 To generate synthetic data, use the following command:
-bash
 py .\testDataGenerator.py -N <Sample size> -f <features> -c <classes> -seed 10000 -output_file generated_data.csv
 
-
 Example:
-
-bash
 py .\testDataGenerator.py -N 150 -f 8 -c 5 -seed 10000 -output_file generated_data.csv
 
 
 ### Step 2: Run the Program
 
 Execute the final LDA model with:
-bash
-python final
+
+py finalLDA
 
 
 ## Model Testing

From 6020b093d4f27c86f9c57b693f2df59002b765c5 Mon Sep 17 00:00:00 2001
From: Rakesh <40319883+rakeshreddy06@users.noreply.github.com>
Date: Thu, 10 Oct 2024 14:57:10 -0500
Subject: [PATCH 06/31] Update README.md

---
 README.md | 11 +++++++++++
 1 file changed, 11 insertions(+)

diff --git a/README.md b/README.md
index 923ceb1..767fa47 100644
--- a/README.md
+++ b/README.md
@@ -17,18 +17,29 @@ This project implements a Linear Discriminant Analysis (LDA) model for dimension
 ### Step 1: Generate the Data
 
 To generate synthetic data, use the following command:
+ ```bash
 py .\testDataGenerator.py -N <Sample size> -f <features> -c <classes> -seed 10000 -output_file generated_data.csv
+ ```
+
+
 
 Example:
+ ```bash
 py .\testDataGenerator.py -N 150 -f 8 -c 5 -seed 10000 -output_file generated_data.csv
 
+ ```
+
 
 ### Step 2: Run the Program
 
 Execute the final LDA model with:
 
+ ```bash
 py finalLDA
 
+ ```
+
+
 
 ## Model Testing
 

From c0248df8cbe689a9914d6066c212caa104d1be7a Mon Sep 17 00:00:00 2001
From: Rakesh <40319883+rakeshreddy06@users.noreply.github.com>
Date: Thu, 10 Oct 2024 14:59:50 -0500
Subject: [PATCH 07/31] Update README.md

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 767fa47..f7edf44 100644
--- a/README.md
+++ b/README.md
@@ -35,7 +35,7 @@ py .\testDataGenerator.py -N 150 -f 8 -c 5 -seed 10000 -output_file generated_da
 Execute the final LDA model with:
 
  ```bash
-py finalLDA
+py .\finalLDA.py
 
  ```
 

From 46c8a268ef35cfbc5272adb17e361d3d2b314cc7 Mon Sep 17 00:00:00 2001
From: Rakesh <40319883+rakeshreddy06@users.noreply.github.com>
Date: Thu, 10 Oct 2024 15:01:05 -0500
Subject: [PATCH 08/31] Update README.md

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index f7edf44..0f36c55 100644
--- a/README.md
+++ b/README.md
@@ -49,7 +49,7 @@ py .\finalLDA.py
 
 ### Step 2: Generated Data
 
-- The model was tested using synthetic data generated from `generated_data.py`, allowing for testing under various conditions with variable classes, samples, and features.
+- The model was tested using synthetic data generated from `testDataGenerator.py`, allowing for testing under various conditions with variable classes, samples, and features.
 
 ### Step 3: Manual Train-Test Split
 

From 6ab5664a3f19f43df65dfb205a2dea844c892529 Mon Sep 17 00:00:00 2001
From: rakeshreddy06 <rakeshreddy5566@gmail.com>
Date: Thu, 10 Oct 2024 15:14:04 -0500
Subject: [PATCH 09/31] next commit

---
 generate_regression_data.py                   | 40 -------------------
 .../models/RegularizedDiscriminantAnalysis.py | 17 --------
 .../models/finalLDA.py                        |  0
 3 files changed, 57 deletions(-)
 delete mode 100644 generate_regression_data.py
 delete mode 100644 regularized_discriminant_analysis/models/RegularizedDiscriminantAnalysis.py
 rename finalLDA.py => regularized_discriminant_analysis/models/finalLDA.py (100%)

diff --git a/generate_regression_data.py b/generate_regression_data.py
deleted file mode 100644
index bca2108..0000000
--- a/generate_regression_data.py
+++ /dev/null
@@ -1,40 +0,0 @@
-import argparse
-import csv
-
-import numpy
-
-def linear_data_generator(m, b, rnge, N, scale, seed):
-  rng = numpy.random.default_rng(seed=seed)
-  sample = rng.uniform(low=rnge[0], high=rnge[1], size=(N, m.shape[0]))
-  ys = numpy.dot(sample, numpy.reshape(m, (-1,1))) + b
-  noise = rng.normal(loc=0., scale=scale, size=ys.shape)
-  return (sample, ys+noise)
-
-def write_data(filename, X, y):
-    with open(filename, "w") as file:
-        # X column for every x
-        xs = [f"x_{n}" for n in range(X.shape[1])]
-        header = xs + ["y"]
-        writer = csv.writer(file)
-        writer.writerow(header)
-        for row in numpy.hstack((X,y)):
-            writer.writerow(row)
-
-def main():
-    parser = argparse.ArgumentParser()
-    parser.add_argument("-N", type = int, help="Number of samples.")
-    parser.add_argument("-m", nargs='*', type = float, help="Expected regression coefficients")
-    parser.add_argument("-b", type= float, help="Offset")
-    parser.add_argument("-scale", type=float, help="Scale of noise")
-    parser.add_argument("-rnge", nargs=2, type=float,  help="Range of Xs")
-    parser.add_argument("-seed", type=int, help="A seed to control randomness")
-    parser.add_argument("-output_file", type=str, help="Path to output file")
-    args = parser.parse_args()
-    m = numpy.array(args.m)
-    X, y = linear_data_generator(m, args.b, args.rnge, args.N, args.scale, args.seed)
-    write_data(args.output_file, X,y)
-
-if __name__=="__main__":
-    main()
-
-
diff --git a/regularized_discriminant_analysis/models/RegularizedDiscriminantAnalysis.py b/regularized_discriminant_analysis/models/RegularizedDiscriminantAnalysis.py
deleted file mode 100644
index 089f9ad..0000000
--- a/regularized_discriminant_analysis/models/RegularizedDiscriminantAnalysis.py
+++ /dev/null
@@ -1,17 +0,0 @@
-
-
-class RDAModel():
-    def __init__(self):
-        pass
-
-
-    def fit(self, X, y):
-        return RDAModelResults()
-
-
-class RDAModelResults():
-    def __init__(self):
-        pass
-
-    def predict(self, x):
-        return 0.5
diff --git a/finalLDA.py b/regularized_discriminant_analysis/models/finalLDA.py
similarity index 100%
rename from finalLDA.py
rename to regularized_discriminant_analysis/models/finalLDA.py

From fc6585c50e5684f8a737c48bfddee3e33b2f7d8b Mon Sep 17 00:00:00 2001
From: rakeshreddy06 <rakeshreddy5566@gmail.com>
Date: Thu, 10 Oct 2024 15:15:30 -0500
Subject: [PATCH 10/31] removed elasticnet

---
 elasticnet/__init__.py                   |  0
 elasticnet/models/ElasticNet.py          | 17 --------
 elasticnet/models/__init__.py            |  0
 elasticnet/tests/small_test.csv          | 51 ------------------------
 elasticnet/tests/test_ElasticNetModel.py | 19 ---------
 5 files changed, 87 deletions(-)
 delete mode 100644 elasticnet/__init__.py
 delete mode 100644 elasticnet/models/ElasticNet.py
 delete mode 100644 elasticnet/models/__init__.py
 delete mode 100644 elasticnet/tests/small_test.csv
 delete mode 100644 elasticnet/tests/test_ElasticNetModel.py

diff --git a/elasticnet/__init__.py b/elasticnet/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/elasticnet/models/ElasticNet.py b/elasticnet/models/ElasticNet.py
deleted file mode 100644
index 017e925..0000000
--- a/elasticnet/models/ElasticNet.py
+++ /dev/null
@@ -1,17 +0,0 @@
-
-
-class ElasticNetModel():
-    def __init__(self):
-        pass
-
-
-    def fit(self, X, y):
-        return ElasticNetModelResults()
-
-
-class ElasticNetModelResults():
-    def __init__(self):
-        pass
-
-    def predict(self, x):
-        return 0.5
diff --git a/elasticnet/models/__init__.py b/elasticnet/models/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/elasticnet/tests/small_test.csv b/elasticnet/tests/small_test.csv
deleted file mode 100644
index bf8442e..0000000
--- a/elasticnet/tests/small_test.csv
+++ /dev/null
@@ -1,51 +0,0 @@
-x_0,x_1,x_2,y
--2.421348566501347,6.290215260063935,2.516304163087373,10.240119830146476
-8.13465811997068,-6.975968662410185,-3.2810945459842866,-6.8962940548446845
--0.4531238994261493,0.05889462611191654,-3.592293253611172,14.10428803155231
-3.979832584128687,-8.129001764124755,9.202914789330517,-43.788867687445624
--4.354231825431758,2.4724749171156333,8.45972163584499,-12.067617018047834
-8.726620980175113,-9.607722575405269,-5.092837184080405,-8.265643240683891
--0.29136484802189955,8.224663789274086,-3.8193339707565555,32.98185595386334
-1.4118708853910462,6.003042800612462,3.9968255952773095,0.7267789346532836
-0.21525181834957507,-3.321041549359367,-5.352746248495515,11.93444109619503
-4.80226153299567,9.818246112545182,4.936296097738831,3.5995719453822046
-9.71733974143089,0.1440918710436101,8.74993701189404,-34.917122745540794
-4.098687611436789,-9.75205878861841,7.980744101999381,-43.32805584620358
--2.398060521804659,2.8278192128541733,-1.626174948927721,16.91539285950553
-5.398272903061114,7.583046908728093,2.758295974535457,4.437457748228852
-3.371527871466675,-5.430064318728407,2.1915998058530857,-16.03565826569788
-2.0863644528269365,0.10824916542728857,8.144465640869694,-25.094326089867696
-2.8255940202840684,-2.286321234798363,4.771241059098381,-18.000440202657604
--8.150227640024978,-4.259315052105519,1.8923353680502952,-1.3930242667026356
--6.067265316809651,3.6776254617776942,8.4817269440159,-10.278522746897893
-8.64017362219969,9.717801217085075,4.980672567111553,-0.9266647796977245
--4.636910653452324,0.9373715699813872,4.978170771263397,-3.8217233698137143
--7.940395120999431,2.953441321061362,-0.9370552302607145,21.291726783530805
-7.692709298116139,-5.485844206553388,-6.019643260327971,2.1873435652525455
--6.485086441297707,7.06589989184231,-8.842925435171665,50.35981404591074
-5.036321300769028,2.0420739888497152,-4.368234397412891,15.435100617505809
--2.203566631709222,-6.141030616852454,-1.822186931753599,-0.5890454529472771
-3.2620868350599768,7.851306022896178,-4.479265977335616,27.896949611024628
-6.402611257683294,-4.018677430646336,0.48600102750762986,-12.289355696825485
-5.378501224056757,4.355667003325474,-7.565417868242747,31.017195148404717
-2.0486633392332614,8.253411759540757,-3.966950647644751,29.555547834722987
-2.626017326894857,3.314924154867276,9.810418858378235,-22.85112181951592
--0.04750452520510429,5.935777040113393,-0.3470621837504506,16.516617979443822
--6.775500897482147,-0.8747563332852692,-2.758815934335188,16.55155644731519
--5.130765599150095,8.959898235120185,1.1701541118251235,22.753375944830324
-9.607901921761815,-9.108821424255002,5.524296399378377,-41.93781490943017
--2.9201254899877434,5.134928295361929,-9.896226148902585,43.58829658171542
-6.956501039100711,0.8359369151964895,-6.1636372998431295,16.225403196517274
-7.725179239543149,-4.913104095867496,-1.110476120153832,-9.936035489824537
--6.142683379729563,1.4244393989902058,1.8529074318076262,5.554396424524908
--2.0474061706133977,-1.2170618863263076,8.899325908803291,-23.596187786238964
-9.359523403637155,3.4124788823300065,-1.4222946765509725,2.4507844709064064
--8.642800876507275,-9.508822574677566,2.9901775243378577,-16.775543378589024
--2.470992582133973,5.1672327675732195,-8.753045094764744,40.855147394263106
--7.756097982925145,5.227601844332813,-3.179199348468109,30.739018818654756
-5.393783291304004,-1.5186710515725927,-7.469139234639499,17.503383657767756
--7.644671911438172,1.8115363641056241,-6.167155079348694,33.57677356652164
-6.557442460132911,-4.44188855380612,-6.368621306151785,7.435670420087931
-0.21009363927752744,-2.719754693698011,1.0885820356480096,-6.289562485886653
--8.571672299069252,8.890348599509473,5.468260371802332,15.412904086362603
-7.872454219630789,-3.9905860234116357,0.9068940749874717,-16.017543419998542
diff --git a/elasticnet/tests/test_ElasticNetModel.py b/elasticnet/tests/test_ElasticNetModel.py
deleted file mode 100644
index 5022c3c..0000000
--- a/elasticnet/tests/test_ElasticNetModel.py
+++ /dev/null
@@ -1,19 +0,0 @@
-import csv
-
-import numpy
-
-from elasticnet.models.ElasticNet import ElasticNetModel
-
-def test_predict():
-    model = ElasticNetModel()
-    data = []
-    with open("small_test.csv", "r") as file:
-        reader = csv.DictReader(file)
-        for row in reader:
-            data.append(row)
-
-    X = numpy.array([[v for k,v in datum.items() if k.startswith('x')] for datum in data])
-    y = numpy.array([[v for k,v in datum.items() if k=='y'] for datum in data])
-    results = model.fit(X,y)
-    preds = results.predict(X)
-    assert preds == 0.5

From 13fe88780335d4a61cfd1b625e849b692675f8be Mon Sep 17 00:00:00 2001
From: rakeshreddy06 <rakeshreddy5566@gmail.com>
Date: Thu, 10 Oct 2024 15:27:38 -0500
Subject: [PATCH 11/31] tree

---
 README.md | 18 ++++++++++++++++++
 1 file changed, 18 insertions(+)

diff --git a/README.md b/README.md
index 0f36c55..52a612e 100644
--- a/README.md
+++ b/README.md
@@ -4,6 +4,7 @@ RakeshReddy - A20525389
 
 ## Overview
 
+
 This project implements a Linear Discriminant Analysis (LDA) model for dimensionality reduction and classification. The model projects high-dimensional data into a lower-dimensional space, maximizing the distance between classes. It is particularly useful for visualizing data with a reduced number of features.
 
 ### When to Use the Model
@@ -12,6 +13,23 @@ This project implements a Linear Discriminant Analysis (LDA) model for dimension
 - **Multivariate Normal Distribution Assumption**: Effective when data for each class follows a normal distribution.
 - **Dimensionality Reduction**: Reduces data to lower dimensions while maintaining class separability.
 
+### Folder Structure
+root:
+│   .gitignore
+│   Final_LDA.ipynb
+│   generated_data.csv
+│   README.md
+│   requirements.txt
+│   testDataGenerator.py
+│
+└───regularized_discriminant_analysis
+    │   test_rdamodel.py
+    │
+    ├───models
+    │       finalLDA.py
+    │
+    └───tests
+
 ## Running the Program
 
 ### Step 1: Generate the Data

From 6bdb751334b97cbabcab47317053c43ba9d457f9 Mon Sep 17 00:00:00 2001
From: rakeshreddy06 <rakeshreddy5566@gmail.com>
Date: Thu, 10 Oct 2024 15:30:25 -0500
Subject: [PATCH 12/31] reverse

---
 README.md | 16 ----------------
 1 file changed, 16 deletions(-)

diff --git a/README.md b/README.md
index 52a612e..8c0a494 100644
--- a/README.md
+++ b/README.md
@@ -13,22 +13,6 @@ This project implements a Linear Discriminant Analysis (LDA) model for dimension
 - **Multivariate Normal Distribution Assumption**: Effective when data for each class follows a normal distribution.
 - **Dimensionality Reduction**: Reduces data to lower dimensions while maintaining class separability.
 
-### Folder Structure
-root:
-│   .gitignore
-│   Final_LDA.ipynb
-│   generated_data.csv
-│   README.md
-│   requirements.txt
-│   testDataGenerator.py
-│
-└───regularized_discriminant_analysis
-    │   test_rdamodel.py
-    │
-    ├───models
-    │       finalLDA.py
-    │
-    └───tests
 
 ## Running the Program
 

From 86e8d6eb10bd027b54dba2598fd6571454fe800b Mon Sep 17 00:00:00 2001
From: Rakesh <40319883+rakeshreddy06@users.noreply.github.com>
Date: Thu, 10 Oct 2024 15:31:11 -0500
Subject: [PATCH 13/31] Update README.md

---
 README.md | 21 +++++++++++++++++++++
 1 file changed, 21 insertions(+)

diff --git a/README.md b/README.md
index 8c0a494..0337aa3 100644
--- a/README.md
+++ b/README.md
@@ -77,3 +77,24 @@ The implementation may encounter difficulties with:
 
 1. **Highly Imbalanced Data**: Struggles when class sizes are noticeably imbalanced.
 2. **High Dimensionality with Few Samples**: May exhibit irregular behavior when the number of features exceeds the number of samples. Dimensionality reduction using PCA can be a solution.
+
+3. # Project Title
+
+A brief description of what this project does and who it's for.
+
+## Project Structure
+C:.
+│   .gitignore
+│   Final_LDA.ipynb
+│   generated_data.csv
+│   README.md
+│   requirements.txt
+│   testDataGenerator.py
+│
+└───regularized_discriminant_analysis
+│   test_rdamodel.py
+│
+├───models
+│       finalLDA.py
+│
+└───tests

From 72f9eea43de21c358d88ecfb351c63752f45e57c Mon Sep 17 00:00:00 2001
From: Rakesh <40319883+rakeshreddy06@users.noreply.github.com>
Date: Thu, 10 Oct 2024 15:31:44 -0500
Subject: [PATCH 14/31] Update README.md

---
 README.md | 21 +--------------------
 1 file changed, 1 insertion(+), 20 deletions(-)

diff --git a/README.md b/README.md
index 0337aa3..98cfb49 100644
--- a/README.md
+++ b/README.md
@@ -78,23 +78,4 @@ The implementation may encounter difficulties with:
 1. **Highly Imbalanced Data**: Struggles when class sizes are noticeably imbalanced.
 2. **High Dimensionality with Few Samples**: May exhibit irregular behavior when the number of features exceeds the number of samples. Dimensionality reduction using PCA can be a solution.
 
-3. # Project Title
-
-A brief description of what this project does and who it's for.
-
-## Project Structure
-C:.
-│   .gitignore
-│   Final_LDA.ipynb
-│   generated_data.csv
-│   README.md
-│   requirements.txt
-│   testDataGenerator.py
-│
-└───regularized_discriminant_analysis
-│   test_rdamodel.py
-│
-├───models
-│       finalLDA.py
-│
-└───tests
+

From 0630446982daf8950b64db1fb67169de96791e2e Mon Sep 17 00:00:00 2001
From: Rakesh <40319883+rakeshreddy06@users.noreply.github.com>
Date: Thu, 10 Oct 2024 16:07:15 -0500
Subject: [PATCH 15/31] Update README.md

---
 README.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/README.md b/README.md
index 98cfb49..2d7b18f 100644
--- a/README.md
+++ b/README.md
@@ -7,6 +7,8 @@ RakeshReddy - A20525389
 
 This project implements a Linear Discriminant Analysis (LDA) model for dimensionality reduction and classification. The model projects high-dimensional data into a lower-dimensional space, maximizing the distance between classes. It is particularly useful for visualizing data with a reduced number of features.
 
+### The program execution steps is shown in final_LDA.ipynb notebook
+
 ### When to Use the Model
 
 - **Classification Tasks**: Suitable for classifying data into different classes.

From 7f2915679c6be2c2375fe5f86b9c3aa0d9af8407 Mon Sep 17 00:00:00 2001
From: Rakesh <40319883+rakeshreddy06@users.noreply.github.com>
Date: Thu, 10 Oct 2024 16:08:14 -0500
Subject: [PATCH 16/31] Update README.md

---
 README.md | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/README.md b/README.md
index 2d7b18f..ff24c4f 100644
--- a/README.md
+++ b/README.md
@@ -42,6 +42,12 @@ Execute the final LDA model with:
 py .\finalLDA.py
 
  ```
+The program execution steps are shown in the notebook file
+ ```bash
+final_LDA.ipynb 
+
+ ```
+
 
 
 

From 898ce9d249426c31d6fd8b2e1eea53874a5d7158 Mon Sep 17 00:00:00 2001
From: Nishant <nishantsk2002@gmail.com>
Date: Thu, 10 Oct 2024 17:27:21 -0500
Subject: [PATCH 17/31] add: pytest testing file added

---
 testing.py | 54 ++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 54 insertions(+)
 create mode 100644 testing.py

diff --git a/testing.py b/testing.py
new file mode 100644
index 0000000..7538bb8
--- /dev/null
+++ b/testing.py
@@ -0,0 +1,54 @@
+import pytest
+import numpy as np
+from Final_LDA import LDAModel, RDAModelResults, manual_train_test_split  
+
+
+# Sample data for testing
+@pytest.fixture
+def sample_data():
+    # Create a small dataset
+    X = np.array([[0.1, 0.2], [0.2, 0.3], [0.1, 0.4], [0.5, 0.6]])
+    y = np.array([0, 0, 1, 1])
+    return X, y
+
+
+def test_lda_model_fit(sample_data):
+    X, y = sample_data
+    model = LDAModel(nComponents=1)
+    model.fit(X, y)
+
+    assert model.Eigens is not None
+    assert model.classMeans is not None
+    assert len(model.classMeans) == len(np.unique(y))
+
+
+def test_lda_model_transform(sample_data):
+    X, y = sample_data
+    model = LDAModel(nComponents=1)
+    model.fit(X, y)
+    transformed_X = model.transform(X)
+
+    assert transformed_X.shape[1] == model.nComponents
+
+
+def test_rda_model_results_predict(sample_data):
+    X, y = sample_data
+    model = LDAModel(nComponents=1)
+    model.fit(X, y)
+    model_results = RDAModelResults(model)
+    y_pred = model_results.predict(X)
+
+    assert y_pred.shape == y.shape
+    assert np.all(np.isin(y_pred, np.unique(y)))  # Predictions should be within the classes
+
+
+def test_manual_train_test_split(sample_data):
+    X, y = sample_data
+    X_train, X_test, y_train, y_test = manual_train_test_split(X, y, test_size=0.5, random_state=42)
+
+    assert X_train.shape[0] + X_test.shape[0] == X.shape[0]
+    assert y_train.shape[0] + y_test.shape[0] == y.shape[0]
+
+
+if __name__ == "__main__":
+    pytest.main()

From e62d613751e5066fd6061ed8b1a66fd9eacb0b8b Mon Sep 17 00:00:00 2001
From: Nishant <nishantsk2002@gmail.com>
Date: Thu, 10 Oct 2024 17:36:55 -0500
Subject: [PATCH 18/31] add: README Updated with names

---
 README.md | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index ff24c4f..47a8534 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,9 @@
 # README
 
-RakeshReddy - A20525389
+Rakesh Reddy - A20525389
+Geeta Hade - A20580824 
+Nishant Khandhar - A20581012
+Amogh Vastrad - A20588808 
 
 ## Overview
 

From ce3dbfe34c3696a69630767c5b79dca8aa7ad3ea Mon Sep 17 00:00:00 2001
From: Rakesh <40319883+rakeshreddy06@users.noreply.github.com>
Date: Thu, 10 Oct 2024 17:38:55 -0500
Subject: [PATCH 19/31] Update README.md

I formatted
---
 README.md | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/README.md b/README.md
index 47a8534..910974a 100644
--- a/README.md
+++ b/README.md
@@ -1,9 +1,9 @@
 # README
 
-Rakesh Reddy - A20525389
-Geeta Hade - A20580824 
-Nishant Khandhar - A20581012
-Amogh Vastrad - A20588808 
+##Rakesh Reddy - A20525389
+##Geeta Hade - A20580824 
+##Nishant Khandhar - A20581012
+##Amogh Vastrad - A20588808 
 
 ## Overview
 

From 6e68e9cf85180f890cf91a14e46bfc9c198d9693 Mon Sep 17 00:00:00 2001
From: Rakesh <40319883+rakeshreddy06@users.noreply.github.com>
Date: Thu, 10 Oct 2024 17:39:16 -0500
Subject: [PATCH 20/31] Update README.md

---
 README.md | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/README.md b/README.md
index 910974a..30c4b90 100644
--- a/README.md
+++ b/README.md
@@ -1,9 +1,9 @@
 # README
 
-##Rakesh Reddy - A20525389
-##Geeta Hade - A20580824 
-##Nishant Khandhar - A20581012
-##Amogh Vastrad - A20588808 
+###Rakesh Reddy - A20525389
+###Geeta Hade - A20580824 
+###Nishant Khandhar - A20581012
+###Amogh Vastrad - A20588808 
 
 ## Overview
 

From 48c7167d98248c61952a48205bbceed40d5b8deb Mon Sep 17 00:00:00 2001
From: Rakesh <40319883+rakeshreddy06@users.noreply.github.com>
Date: Thu, 10 Oct 2024 17:39:38 -0500
Subject: [PATCH 21/31] Update README.md

---
 README.md | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/README.md b/README.md
index 30c4b90..07e7385 100644
--- a/README.md
+++ b/README.md
@@ -1,16 +1,16 @@
 # README
 
-###Rakesh Reddy - A20525389
-###Geeta Hade - A20580824 
-###Nishant Khandhar - A20581012
-###Amogh Vastrad - A20588808 
+
 
 ## Overview
 
 
 This project implements a Linear Discriminant Analysis (LDA) model for dimensionality reduction and classification. The model projects high-dimensional data into a lower-dimensional space, maximizing the distance between classes. It is particularly useful for visualizing data with a reduced number of features.
 
-### The program execution steps is shown in final_LDA.ipynb notebook
+###Rakesh Reddy - A20525389
+###Geeta Hade - A20580824 
+###Nishant Khandhar - A20581012
+###Amogh Vastrad - A20588808 
 
 ### When to Use the Model
 

From 006a480b3ed48e93ed11785b1c9c3e333ecd2e01 Mon Sep 17 00:00:00 2001
From: Rakesh <40319883+rakeshreddy06@users.noreply.github.com>
Date: Thu, 10 Oct 2024 17:40:09 -0500
Subject: [PATCH 22/31] Update README.md

---
 README.md | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/README.md b/README.md
index 07e7385..f9c3a02 100644
--- a/README.md
+++ b/README.md
@@ -1,16 +1,16 @@
 # README
 
-
+Rakesh Reddy - A20525389
+Geeta Hade - A20580824 
+Nishant Khandhar - A20581012
+Amogh Vastrad - A20588808 
 
 ## Overview
 
 
 This project implements a Linear Discriminant Analysis (LDA) model for dimensionality reduction and classification. The model projects high-dimensional data into a lower-dimensional space, maximizing the distance between classes. It is particularly useful for visualizing data with a reduced number of features.
 
-###Rakesh Reddy - A20525389
-###Geeta Hade - A20580824 
-###Nishant Khandhar - A20581012
-###Amogh Vastrad - A20588808 
+
 
 ### When to Use the Model
 

From e7cf6a043197cf98b2bee0b91a5cefd046d03b75 Mon Sep 17 00:00:00 2001
From: Rakesh <40319883+rakeshreddy06@users.noreply.github.com>
Date: Thu, 10 Oct 2024 17:42:30 -0500
Subject: [PATCH 23/31] Update README.md

---
 README.md | 12 ++++++++----
 1 file changed, 8 insertions(+), 4 deletions(-)

diff --git a/README.md b/README.md
index f9c3a02..fe119c1 100644
--- a/README.md
+++ b/README.md
@@ -1,9 +1,13 @@
 # README
+**Course:** CS584 - Machine Learning <br>
+**Instructor:** Steve Avsec<br>
+**Group Members:**
+- Rakesh Reddy - A20525389
+- Geeta Hade - A20580824 
+- Nishant Khandhar - A20581012
+- Amogh Vastrad - A20588808 
+
 
-Rakesh Reddy - A20525389
-Geeta Hade - A20580824 
-Nishant Khandhar - A20581012
-Amogh Vastrad - A20588808 
 
 ## Overview
 

From 6bdb5c4dd18f875e3a7b13c33577d8e617205863 Mon Sep 17 00:00:00 2001
From: Rakesh <40319883+rakeshreddy06@users.noreply.github.com>
Date: Thu, 10 Oct 2024 17:42:54 -0500
Subject: [PATCH 24/31] Update README.md

---
 README.md | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/README.md b/README.md
index fe119c1..1950238 100644
--- a/README.md
+++ b/README.md
@@ -1,7 +1,6 @@
 # README
-**Course:** CS584 - Machine Learning <br>
-**Instructor:** Steve Avsec<br>
-**Group Members:**
+
+**TEAM:**
 - Rakesh Reddy - A20525389
 - Geeta Hade - A20580824 
 - Nishant Khandhar - A20581012

From 7a313cf22f09df647a904f0188b7787cd38fee4f Mon Sep 17 00:00:00 2001
From: Rakesh <40319883+rakeshreddy06@users.noreply.github.com>
Date: Thu, 10 Oct 2024 20:50:55 -0500
Subject: [PATCH 25/31] Update README.md

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 1950238..e80bca1 100644
--- a/README.md
+++ b/README.md
@@ -42,7 +42,7 @@ py .\testDataGenerator.py -N 150 -f 8 -c 5 -seed 10000 -output_file generated_da
 
 ### Step 2: Run the Program
 
-Execute the final LDA model with:
+Execute the final LDA model which is model folder:
 
  ```bash
 py .\finalLDA.py

From 82dcefa0ea3b9ad59dce7cf1aca5a066ae62e4b5 Mon Sep 17 00:00:00 2001
From: Rakesh <40319883+rakeshreddy06@users.noreply.github.com>
Date: Fri, 11 Oct 2024 10:36:36 -0500
Subject: [PATCH 26/31] added a note to the read me

---
 README.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/README.md b/README.md
index e80bca1..bdcc2fb 100644
--- a/README.md
+++ b/README.md
@@ -43,6 +43,7 @@ py .\testDataGenerator.py -N 150 -f 8 -c 5 -seed 10000 -output_file generated_da
 ### Step 2: Run the Program
 
 Execute the final LDA model which is model folder:
+Note: Place the generated_data.csv(the data you want to train and test ) in the models folder to run this program else you can run Final_LDA.ipynb directly without any changes by placing the training data named as generated_data.csv.
 
  ```bash
 py .\finalLDA.py

From 3ba962193ec15943e732ea63dd3181d70b5f0233 Mon Sep 17 00:00:00 2001
From: Rakesh <40319883+rakeshreddy06@users.noreply.github.com>
Date: Mon, 14 Oct 2024 18:44:10 -0500
Subject: [PATCH 27/31] making improvement

---
 regularized_discriminant_analysis/models/finalLDA.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/regularized_discriminant_analysis/models/finalLDA.py b/regularized_discriminant_analysis/models/finalLDA.py
index 348a1e4..31cbf84 100644
--- a/regularized_discriminant_analysis/models/finalLDA.py
+++ b/regularized_discriminant_analysis/models/finalLDA.py
@@ -139,7 +139,7 @@ def manual_train_test_split(X, y, test_size=0.2, random_state=None):
     
     return X[train_indices], X[test_indices], y[train_indices], y[test_indices]
 
-df = pd.read_csv("generated_data.csv", header=None)
+df = pd.read_csv("../../generated_data.csv", header=None)
 
 X_features = df.iloc[:, :-1].values #this is the training data
 y_labels= df .iloc[:, -1].values #this is the training labels

From 2e347d449c2a289a1980d01802ea9a2468ad0585 Mon Sep 17 00:00:00 2001
From: Rakesh <40319883+rakeshreddy06@users.noreply.github.com>
Date: Mon, 14 Oct 2024 18:48:42 -0500
Subject: [PATCH 28/31] Update finalLDA.py

---
 regularized_discriminant_analysis/models/finalLDA.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/regularized_discriminant_analysis/models/finalLDA.py b/regularized_discriminant_analysis/models/finalLDA.py
index 31cbf84..348a1e4 100644
--- a/regularized_discriminant_analysis/models/finalLDA.py
+++ b/regularized_discriminant_analysis/models/finalLDA.py
@@ -139,7 +139,7 @@ def manual_train_test_split(X, y, test_size=0.2, random_state=None):
     
     return X[train_indices], X[test_indices], y[train_indices], y[test_indices]
 
-df = pd.read_csv("../../generated_data.csv", header=None)
+df = pd.read_csv("generated_data.csv", header=None)
 
 X_features = df.iloc[:, :-1].values #this is the training data
 y_labels= df .iloc[:, -1].values #this is the training labels

From 819833aa9143b9b2220e39cb58d4064c79324f14 Mon Sep 17 00:00:00 2001
From: Rakesh <40319883+rakeshreddy06@users.noreply.github.com>
Date: Mon, 14 Oct 2024 18:49:51 -0500
Subject: [PATCH 29/31] added generated_data to test on more data

---
 .../models/generated_data.csv                 | 750 ++++++++++++++++++
 1 file changed, 750 insertions(+)
 create mode 100644 regularized_discriminant_analysis/models/generated_data.csv

diff --git a/regularized_discriminant_analysis/models/generated_data.csv b/regularized_discriminant_analysis/models/generated_data.csv
new file mode 100644
index 0000000..24c4346
--- /dev/null
+++ b/regularized_discriminant_analysis/models/generated_data.csv
@@ -0,0 +1,750 @@
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From deae62a1c748fd8eabac1450851d2e12b8765a9b Mon Sep 17 00:00:00 2001
From: Rakesh <40319883+rakeshreddy06@users.noreply.github.com>
Date: Mon, 14 Oct 2024 18:54:32 -0500
Subject: [PATCH 30/31] Update README.md

---
 README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index bdcc2fb..bdfae11 100644
--- a/README.md
+++ b/README.md
@@ -43,7 +43,7 @@ py .\testDataGenerator.py -N 150 -f 8 -c 5 -seed 10000 -output_file generated_da
 ### Step 2: Run the Program
 
 Execute the final LDA model which is model folder:
-Note: Place the generated_data.csv(the data you want to train and test ) in the models folder to run this program else you can run Final_LDA.ipynb directly without any changes by placing the training data named as generated_data.csv.
+Note:  Final_LDA.ipynb is our  final output and notebook  fine
 
  ```bash
 py .\finalLDA.py
@@ -51,7 +51,7 @@ py .\finalLDA.py
  ```
 The program execution steps are shown in the notebook file
  ```bash
-final_LDA.ipynb 
+Final_LDA.ipynb 
 
  ```
 

From 41f9506f397ce7644275c43ba7be0dff29d89171 Mon Sep 17 00:00:00 2001
From: Rakesh <40319883+rakeshreddy06@users.noreply.github.com>
Date: Mon, 14 Oct 2024 19:37:19 -0500
Subject: [PATCH 31/31] Update README.md

---
 README.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/README.md b/README.md
index bdfae11..966653a 100644
--- a/README.md
+++ b/README.md
@@ -42,11 +42,11 @@ py .\testDataGenerator.py -N 150 -f 8 -c 5 -seed 10000 -output_file generated_da
 
 ### Step 2: Run the Program
 
-Execute the final LDA model which is model folder:
-Note:  Final_LDA.ipynb is our  final output and notebook  fine
+Execute the final LDA model:
+Note:  Final_LDA.ipynb is our  final code and notebook  file to test our program,
 
  ```bash
-py .\finalLDA.py
+Final_LDA.ipynb 
 
  ```
 The program execution steps are shown in the notebook file