Machine Learning Algorithms in Go

In this repository, we have implemented several machine learning algorithms using Go. The algorithms included are:

• K-Nearest Neighbors (KNN)
• Neural Networks
• Decision Trees
• Support Vector Machines (SVM)
• Principal Component Analysis (PCA)
• Recurrent Neural Networks (RNN)

K-Nearest Neighbors (KNN)

K-Nearest Neighbors, or KNN, is a non-parametric classification algorithm. It works by finding the K closest data points in the training set to a given test point and classifying the test point based on the classes of its nearest neighbors. KNN can be used for both classification and regression tasks.

Mathematical Explanation

Given a training set $X = {x_1, x_2, ..., x_n}$ and corresponding labels $Y = {y_1, y_2, ..., y_n}$, KNN works as follows:

1. Calculate the distance between the test instance and each instance in the training set.
2. Select the k-nearest neighbors based on the calculated distances.
3. Assign the test instance to the class that appears most frequently among the k-nearest neighbors.

The distance metric used can be Euclidean distance, Manhattan distance, etc. The value of k is usually chosen through cross-validation.

Neural Networks

Neural networks are a class of machine learning algorithms inspired by the structure and function of the human brain. They are composed of layers of interconnected nodes, called neurons, that perform simple computations on their inputs.

Mathematical Explanation

A neural network with one hidden layer can be represented as follows:

$$ h = f(W_1x + b_1) \\ y = softmax(W_2h + b_2) $$

where $x$ is the input, $h$ is the hidden layer, $y$ is the output, $W_1$ and $W_2$ are weight matrices, $b_1$ and $b_2$ are bias vectors, $f$ is an activation function (e.g. ReLU, sigmoid), and softmax is a function that normalizes the output scores to probabilities.

The weights and biases are learned through backpropagation, which involves computing the gradients of the loss function with respect to the parameters and using them to update the parameters iteratively.

Decision Trees

Decision trees are a popular class of algorithms for both classification and regression tasks. They work by recursively partitioning the feature space into regions based on the values of the features, until all instances in a region belong to the same class or have similar output values.

Mathematical Explanation

A decision tree can be represented as a binary tree, where each internal node represents a decision based on a feature value, and each leaf node represents a class label or output value.

The splitting criterion used can be based on information gain, Gini impurity, or other measures. The tree is constructed recursively by selecting the best split at each internal node based on the chosen criterion.

Support Vector Machines (SVM)

Support Vector Machines, or SVMs, are a class of algorithms for binary classification tasks. They work by finding the hyperplane that maximally separates the two classes in the feature space, while minimizing the margin between the hyperplane and the closest points from each class.

Mathematical Explanation

Given a training set $X = {x_1, x_2, ..., x_n}$ and corresponding labels $Y = {y_1, y_2, ..., y_n}$, SVM works as follows:

1. Find the hyperplane that maximizes the margin between the two classes: $w^Tx + b = 0$
2. Classify a new instance based on its position relative to the hyperplane: $y = sign(w^Tx + b)$

The optimal hyperplane is found by solving a convex optimization problem, where the objective function is to maximize the margin subject to the constraint that all instances are correctly classified. In practice, non-linear SVMs are often used by mapping the input to a higher-dimensional feature space using a kernel function.

Principal Component Analysis (PCA)

Principal Component Analysis, or PCA, is a dimensionality reduction technique that finds the directions of maximum variance in the data and projects the data onto a lower-dimensional subspace spanned by these directions.

Mathematical Explanation

Given a dataset $X$ with $n$ instances and $p$ features, PCA works as follows:

1. Standardize the dataset by subtracting the mean and dividing by the standard deviation.
2. Compute the covariance matrix: $\Sigma = \frac{1}{n-1}X^TX$
3. Compute the eigenvectors and eigenvalues of the covariance matrix.
4. Sort the eigenvectors in descending order based on their corresponding eigenvalues.
5. Select the top $k$ eigenvectors as the principal components and project the data onto this subspace.

PCA can be used for data visualization, noise reduction, and feature extraction.

Recurrent Neural Networks (RNN)

Recurrent Neural Networks, or RNNs, are a class of neural networks that can handle sequential data by maintaining a hidden state that captures information from previous inputs. They have been successfully applied to tasks such as language modeling, speech recognition, and image captioning.

Mathematical Explanation

A simple RNN can be represented as follows:

$$ h_t = f(W_{hh}h_{t-1} + W_{xh}x_t + b_h) \\ y_t = g(W_{hy}h_t + b_y) $$

where $x_t$ is the input at time step $t$, $h_t$ is the hidden state at time step $t$, $y_t$ is the output at time step $t$, $W$ and $b$ are weight matrices and bias vectors, and $f$ and $g$ are activation functions (e.g. tanh, sigmoid).

The weights and biases are learned through backpropagation through time, which involves computing the gradients of the loss function with respect to the parameters at each time step and using them to update the parameters iteratively.

Conclusion

These are some of the most commonly used machine learning algorithms in practice. While there are many other algorithms out there, understanding these fundamental models will give you a solid foundation to build upon.

[🧠)