/****************************************************** * File: visualizer.cpp * Author: Keith Schwarz (htiek@cs.stanford.edu) * * Implementation of the Perceptron visualizer. This code * generates an HTML document colored according to the relative * weights used by the perceptron network. Brighter colors * indicate positive weight, negative colors negative weight. */ #include "visualizer.h" #include #include #include #include #include using namespace std; /** Module Constants **/ const size_t kImageDimension = 28; // Size of the image on one axis const size_t kImageSize = kImageDimension * kImageDimension; // Total image size /* Function to convert an integer to a string. This is based on the implementation * from earlier lecture code. */ static string IntegerToString(int value) { stringstream converter; converter << value; return converter.str(); } /* Given a value and the minimum and maximum values used by the perceptron, * returns a hex triplet (i.e. #ffffff) containing a color for that value. * This works by normalizing the value to the range 0 - 1 and then scaling * that to a value between 0 - 255 and taking the hex encoding. */ static string GetColor(double value, double min, double max) { /* Normalize to a value between 0 and 1 */ value -= min; value /= (max - min); /* Scale to 0 - 255 */ int scaled = 255 * value; /* Use a stringstream to format the data properly. */ stringstream result; result << setfill('0') << hex // Write in hex with 0 padding. << '#' << setw(2) << scaled // Use two characters and print out << setw(2) << scaled << setw(2) << scaled; return result.str(); } /* Generates the HTML document of the visualization. The HTML document will * contain a table where each cell of the table is colored based on the * perceptron's weights. */ void SaveVisualization(vector& classifier, int index) { /* Ensure we have enough data (one element per pixel, plus one for the * constant term. */ assert (classifier.size() == kImageSize + 1); /* Our filename is [index].html; i.e. 0.html, 1.html, etc. */ string filename = IntegerToString(index) + ".html"; ofstream output((IntegerToString(index) + ".html").c_str()); /* Get the maximum and minimum values for normalization. This uses the * min_element and max_element algorithms, which we'll cover in a few * weeks. */ const double minimum = *min_element(classifier.begin(), classifier.end()); const double maximum = *max_element(classifier.begin(), classifier.end()); /* Start generating the HTML. */ output << "\n\t\n\t\t Perceptron for " << index << " \n\t" << endl; output << "\t\n\t\t" << endl; /* Iterate across the rows and columns of the perceptron rending our findings. */ for (size_t y = 0; y < kImageDimension; ++y) { output << "\t\t\t" << endl; for (size_t x = 0; x < kImageDimension; ++x) { /* Print a comment containing the actual value. */ output << "" << endl; /* Print out the actual cell. */ output << "\t\t\t\t" << endl; } output << "\t\t\t" << endl; } /* Close the table. */ output << "\t\t
" << endl; /* Add a link to the next image. */ output << "\t\tNext" << endl; /* Finish the document */ output << "\t\n\t" << endl; }