#Implementing an SVM for Handwriting Recognition

##Quick Start

Download the zip file containing the SVM files. Use the train.py function to train and the test.py function to test.

##Installation

###Python Dependencies

numpy >= 1.10.4

cvxopt >= 1.1.8

##Running the code with default settings

In order to train the SVM, run the train.py python file with two arguments X_train and Y_train, where X_train is the input training data and Y_train is the desired output vector.

python train.py X_train Y_train

In order to test the SVM, run the test.py python file with one argument X_test, when the X_test is the input testing data points.

python test.py X_test

The result of test.py will be a vector with the predicted classification of each input testing data point.

##Parameters

trainingSampleSize: Training sample size (Default value: 1200)

numBootstraps: Number of bootstrap samples per class (Default value: 1)

bootstrapSampleSize: Bootstrap sample size (Default value: math.floor(0.7 * trainingSampleSize))

testSize: Number of points in the test set (Default value: 500)

minConfidence: Minimum confidence needed for a class prediction (Default value: 0.1)

C: Tradeoff parameter for the slack variables (Default value: 4)

minSupportVector: Minimum value for point to be considered a support vector (Default value: 0.1)

RBF_sigma: The variance of the kernel (Default value: 1)

kernel: The kernel used in the calculations (Default value: kernels.rbf(RBF_sigma))