mirror of https://github.com/TheAlgorithms/C
[doc fix] fix documentations in k_means (#592)
* fix documentations
* clang-tidy fixes for 814f9077b7
Co-authored-by: github-actions <${GITHUB_ACTOR}@users.noreply.github.com>
This commit is contained in:
Krishna Vedala
GitHub
parent
05d9af45f3
commit
0b426c0124
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@ -35,10 +35,11 @@
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* the name observation is used to denote
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* a random point in plane
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*/
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typedef struct observation {
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double x; /**< abscissa of 2D data point */
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double y; /**< ordinate of 2D data point */
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int group; /**< the group no in which this observation would go */
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typedef struct observation
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{
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double x; /**< abscissa of 2D data point */
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double y; /**< ordinate of 2D data point */
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int group; /**< the group no in which this observation would go */
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} observation;
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/*! @struct cluster
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@ -48,13 +49,14 @@ typedef struct observation {
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* stores the count of observations
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* belonging to this cluster
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*/
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typedef struct cluster {
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double x; /**< abscissa centroid of this cluster */
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double y; /**< ordinate of centroid of this cluster */
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size_t count; /**< count of observations present in this cluster */
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typedef struct cluster
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{
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double x; /**< abscissa centroid of this cluster */
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double y; /**< ordinate of centroid of this cluster */
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size_t count; /**< count of observations present in this cluster */
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} cluster;
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/*! @fn calculateNearest
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/*!
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* Returns the index of centroid nearest to
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* given observation
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*
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@ -64,24 +66,27 @@ typedef struct cluster {
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*
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* @returns the index of nearest centroid for given observation
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*/
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int calculateNearst(observation* o, cluster clusters[], int k) {
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double minD = DBL_MAX;
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double dist = 0;
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int index = -1;
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int i = 0;
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for (; i < k; i++) {
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/* Calculate Squared Distance*/
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dist = (clusters[i].x - o->x) * (clusters[i].x - o->x) +
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(clusters[i].y - o->y) * (clusters[i].y - o->y);
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if (dist < minD) {
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minD = dist;
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index = i;
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int calculateNearst(observation* o, cluster clusters[], int k)
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{
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double minD = DBL_MAX;
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double dist = 0;
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int index = -1;
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int i = 0;
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for (; i < k; i++)
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{
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/* Calculate Squared Distance*/
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dist = (clusters[i].x - o->x) * (clusters[i].x - o->x) +
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(clusters[i].y - o->y) * (clusters[i].y - o->y);
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if (dist < minD)
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{
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minD = dist;
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index = i;
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}
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}
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}
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return index;
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return index;
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}
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/*! @fn calculateCentroid
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/*!
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* Calculate centoid and assign it to the cluster variable
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*
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* @param observations an array of observations whose centroid is calculated
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@ -90,22 +95,24 @@ int calculateNearst(observation* o, cluster clusters[], int k) {
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* centroid
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*/
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void calculateCentroid(observation observations[], size_t size,
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cluster* centroid) {
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size_t i = 0;
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centroid->x = 0;
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centroid->y = 0;
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centroid->count = size;
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for (; i < size; i++) {
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centroid->x += observations[i].x;
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centroid->y += observations[i].y;
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observations[i].group = 0;
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}
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centroid->x /= centroid->count;
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centroid->y /= centroid->count;
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cluster* centroid)
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{
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size_t i = 0;
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centroid->x = 0;
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centroid->y = 0;
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centroid->count = size;
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for (; i < size; i++)
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{
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centroid->x += observations[i].x;
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centroid->y += observations[i].y;
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observations[i].group = 0;
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}
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centroid->x /= centroid->count;
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centroid->y /= centroid->count;
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}
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/*! @fn kMeans
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* --K Means Algorithm--
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/*!
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* --K Means Algorithm--
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* 1. Assign each observation to one of k groups
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* creating a random initial clustering
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* 2. Find the centroid of observations for each
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@ -117,77 +124,93 @@ void calculateCentroid(observation observations[], size_t size,
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* 5. Repeat step 2,3,4 until there is no change
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* the current clustering and is same as last
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* clustering.
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*
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* @param observations an array of observations to cluster
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* @param size size of observations array
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* @param k no of clusters to be made
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*
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* @returns pointer to cluster object
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*/
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cluster* kMeans(observation observations[], size_t size, int k) {
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cluster* clusters = NULL;
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if (k <= 1) {
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/*
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If we have to cluster them only in one group
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then calculate centroid of observations and
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that will be a ingle cluster
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*/
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clusters = (cluster*)malloc(sizeof(cluster));
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memset(clusters, 0, sizeof(cluster));
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calculateCentroid(observations, size, clusters);
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} else if (k < size) {
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clusters = malloc(sizeof(cluster) * k);
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memset(clusters, 0, k * sizeof(cluster));
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/* STEP 1 */
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for (size_t j = 0; j < size; j++) {
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observations[j].group = rand() % k;
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cluster* kMeans(observation observations[], size_t size, int k)
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{
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cluster* clusters = NULL;
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if (k <= 1)
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{
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/*
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If we have to cluster them only in one group
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then calculate centroid of observations and
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that will be a ingle cluster
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*/
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clusters = (cluster*)malloc(sizeof(cluster));
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memset(clusters, 0, sizeof(cluster));
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calculateCentroid(observations, size, clusters);
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}
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size_t changed = 0;
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size_t minAcceptedError =
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size / 10000; // Do until 99.99 percent points are in correct cluster
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int t = 0;
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do {
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/* Initialize clusters */
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for (int i = 0; i < k; i++) {
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clusters[i].x = 0;
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clusters[i].y = 0;
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clusters[i].count = 0;
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}
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/* STEP 2*/
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for (size_t j = 0; j < size; j++) {
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t = observations[j].group;
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clusters[t].x += observations[j].x;
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clusters[t].y += observations[j].y;
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clusters[t].count++;
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}
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for (int i = 0; i < k; i++) {
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clusters[i].x /= clusters[i].count;
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clusters[i].y /= clusters[i].count;
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}
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/* STEP 3 and 4 */
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changed = 0; // this variable stores change in clustering
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for (size_t j = 0; j < size; j++) {
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t = calculateNearst(observations + j, clusters, k);
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if (t != observations[j].group) {
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changed++;
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observations[j].group = t;
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else if (k < size)
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{
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clusters = malloc(sizeof(cluster) * k);
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memset(clusters, 0, k * sizeof(cluster));
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/* STEP 1 */
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for (size_t j = 0; j < size; j++)
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{
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observations[j].group = rand() % k;
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}
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}
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} while (changed > minAcceptedError); // Keep on grouping until we have
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// got almost best clustering
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} else {
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/* If no of clusters is more than observations
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each observation can be its own cluster
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*/
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clusters = (cluster*)malloc(sizeof(cluster) * k);
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memset(clusters, 0, k * sizeof(cluster));
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for (int j = 0; j < size; j++) {
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clusters[j].x = observations[j].x;
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clusters[j].y = observations[j].y;
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clusters[j].count = 1;
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observations[j].group = j;
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size_t changed = 0;
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size_t minAcceptedError =
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size /
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10000; // Do until 99.99 percent points are in correct cluster
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int t = 0;
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do
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{
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/* Initialize clusters */
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for (int i = 0; i < k; i++)
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{
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clusters[i].x = 0;
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clusters[i].y = 0;
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clusters[i].count = 0;
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}
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/* STEP 2*/
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for (size_t j = 0; j < size; j++)
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{
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t = observations[j].group;
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clusters[t].x += observations[j].x;
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clusters[t].y += observations[j].y;
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clusters[t].count++;
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}
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for (int i = 0; i < k; i++)
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{
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clusters[i].x /= clusters[i].count;
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clusters[i].y /= clusters[i].count;
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}
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/* STEP 3 and 4 */
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changed = 0; // this variable stores change in clustering
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for (size_t j = 0; j < size; j++)
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{
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t = calculateNearst(observations + j, clusters, k);
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if (t != observations[j].group)
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{
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changed++;
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observations[j].group = t;
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}
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}
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} while (changed > minAcceptedError); // Keep on grouping until we have
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// got almost best clustering
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}
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}
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return clusters;
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else
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{
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/* If no of clusters is more than observations
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each observation can be its own cluster
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*/
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clusters = (cluster*)malloc(sizeof(cluster) * k);
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memset(clusters, 0, k * sizeof(cluster));
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for (int j = 0; j < size; j++)
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{
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clusters[j].x = observations[j].x;
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clusters[j].y = observations[j].y;
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clusters[j].count = 1;
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observations[j].group = j;
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}
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}
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return clusters;
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}
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/**
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@ -195,73 +218,96 @@ cluster* kMeans(observation observations[], size_t size, int k) {
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* @}
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*/
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/*! @fn printEPS
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/*!
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* A function to print observations and clusters
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* The code is taken from
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* @link http://rosettacode.org/wiki/K-means%2B%2B_clustering
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* its C implementation
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* http://rosettacode.org/wiki/K-means%2B%2B_clustering.
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* Even the K Means code is also inspired from it
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*
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* Note: To print in a file use pipeline operator ( ./a.out > image.eps )
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* @note To print in a file use pipeline operator
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* ```sh
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* ./k_means_clustering > image.eps
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* ```
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*
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* @param observations observations array
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* @param len size of observation array
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* @param cent clusters centroid's array
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* @param k size of cent array
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*/
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void printEPS(observation pts[], size_t len, cluster cent[], int k) {
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int W = 400, H = 400;
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double min_x = DBL_MAX, max_x = DBL_MIN, min_y = DBL_MAX, max_y = DBL_MIN;
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double scale = 0, cx = 0, cy = 0;
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double* colors = (double*)malloc(sizeof(double) * (k * 3));
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int i;
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size_t j;
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double kd = k * 1.0;
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for (i = 0; i < k; i++) {
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*(colors + 3 * i) = (3 * (i + 1) % k) / kd;
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*(colors + 3 * i + 1) = (7 * i % k) / kd;
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*(colors + 3 * i + 2) = (9 * i % k) / kd;
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}
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for (j = 0; j < len; j++) {
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if (max_x < pts[j].x) max_x = pts[j].x;
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if (min_x > pts[j].x) min_x = pts[j].x;
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if (max_y < pts[j].y) max_y = pts[j].y;
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if (min_y > pts[j].y) min_y = pts[j].y;
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}
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scale = W / (max_x - min_x);
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if (scale > (H / (max_y - min_y))) {
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scale = H / (max_y - min_y);
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};
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cx = (max_x + min_x) / 2;
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cy = (max_y + min_y) / 2;
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printf("%%!PS-Adobe-3.0 EPSF-3.0\n%%%%BoundingBox: -5 -5 %d %d\n", W + 10,
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H + 10);
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printf(
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"/l {rlineto} def /m {rmoveto} def\n"
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"/c { .25 sub exch .25 sub exch .5 0 360 arc fill } def\n"
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"/s { moveto -2 0 m 2 2 l 2 -2 l -2 -2 l closepath "
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" gsave 1 setgray fill grestore gsave 3 setlinewidth"
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" 1 setgray stroke grestore 0 setgray stroke }def\n");
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for (int i = 0; i < k; i++) {
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printf("%g %g %g setrgbcolor\n", *(colors + 3 * i), *(colors + 3 * i + 1),
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*(colors + 3 * i + 2));
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for (j = 0; j < len; j++) {
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if (pts[j].group != i) continue;
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printf("%.3f %.3f c\n", (pts[j].x - cx) * scale + W / 2,
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(pts[j].y - cy) * scale + H / 2);
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void printEPS(observation pts[], size_t len, cluster cent[], int k)
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{
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int W = 400, H = 400;
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double min_x = DBL_MAX, max_x = DBL_MIN, min_y = DBL_MAX, max_y = DBL_MIN;
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double scale = 0, cx = 0, cy = 0;
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double* colors = (double*)malloc(sizeof(double) * (k * 3));
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int i;
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size_t j;
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double kd = k * 1.0;
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for (i = 0; i < k; i++)
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{
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*(colors + 3 * i) = (3 * (i + 1) % k) / kd;
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*(colors + 3 * i + 1) = (7 * i % k) / kd;
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*(colors + 3 * i + 2) = (9 * i % k) / kd;
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}
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printf("\n0 setgray %g %g s\n", (cent[i].x - cx) * scale + W / 2,
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(cent[i].y - cy) * scale + H / 2);
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}
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printf("\n%%%%EOF");
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// free accquired memory
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free(colors);
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for (j = 0; j < len; j++)
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{
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if (max_x < pts[j].x)
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{
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max_x = pts[j].x;
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}
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if (min_x > pts[j].x)
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{
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min_x = pts[j].x;
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}
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if (max_y < pts[j].y)
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{
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max_y = pts[j].y;
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}
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if (min_y > pts[j].y)
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{
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min_y = pts[j].y;
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}
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}
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scale = W / (max_x - min_x);
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if (scale > (H / (max_y - min_y)))
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{
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scale = H / (max_y - min_y);
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};
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cx = (max_x + min_x) / 2;
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cy = (max_y + min_y) / 2;
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printf("%%!PS-Adobe-3.0 EPSF-3.0\n%%%%BoundingBox: -5 -5 %d %d\n", W + 10,
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H + 10);
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printf(
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"/l {rlineto} def /m {rmoveto} def\n"
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"/c { .25 sub exch .25 sub exch .5 0 360 arc fill } def\n"
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"/s { moveto -2 0 m 2 2 l 2 -2 l -2 -2 l closepath "
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" gsave 1 setgray fill grestore gsave 3 setlinewidth"
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" 1 setgray stroke grestore 0 setgray stroke }def\n");
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for (int i = 0; i < k; i++)
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{
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printf("%g %g %g setrgbcolor\n", *(colors + 3 * i),
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*(colors + 3 * i + 1), *(colors + 3 * i + 2));
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for (j = 0; j < len; j++)
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{
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if (pts[j].group != i)
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{
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continue;
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}
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printf("%.3f %.3f c\n", (pts[j].x - cx) * scale + W / 2,
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(pts[j].y - cy) * scale + H / 2);
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}
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printf("\n0 setgray %g %g s\n", (cent[i].x - cx) * scale + W / 2,
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(cent[i].y - cy) * scale + H / 2);
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}
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printf("\n%%%%EOF");
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// free accquired memory
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free(colors);
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}
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/*! @fn test
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/*!
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* A function to test the kMeans function
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* Generates 100000 points in a circle of
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* radius 20.0 with center at (0,0)
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@ -270,29 +316,33 @@ void printEPS(observation pts[], size_t len, cluster cent[], int k) {
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* <img alt="Output for 100000 points divided in 5 clusters" src=
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* "https://raw.githubusercontent.com/TheAlgorithms/C/docs/images/machine_learning/k_means_clustering/kMeansTest1.png"
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* width="400px" heiggt="400px">
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* @returns None
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*/
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static void test() {
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size_t size = 100000L;
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observation* observations = (observation*)malloc(sizeof(observation) * size);
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double maxRadius = 20.00;
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double radius = 0;
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double ang = 0;
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size_t i = 0;
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for (; i < size; i++) {
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radius = maxRadius * ((double)rand() / RAND_MAX);
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ang = 2 * M_PI * ((double)rand() / RAND_MAX);
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observations[i].x = radius * cos(ang);
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observations[i].y = radius * sin(ang);
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}
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int k = 5; // No of clusters
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cluster* clusters = kMeans(observations, size, k);
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printEPS(observations, size, clusters, k);
|
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// Free the accquired memory
|
||||
free(observations);
|
||||
free(clusters);
|
||||
static void test()
|
||||
{
|
||||
size_t size = 100000L;
|
||||
observation* observations =
|
||||
(observation*)malloc(sizeof(observation) * size);
|
||||
double maxRadius = 20.00;
|
||||
double radius = 0;
|
||||
double ang = 0;
|
||||
size_t i = 0;
|
||||
for (; i < size; i++)
|
||||
{
|
||||
radius = maxRadius * ((double)rand() / RAND_MAX);
|
||||
ang = 2 * M_PI * ((double)rand() / RAND_MAX);
|
||||
observations[i].x = radius * cos(ang);
|
||||
observations[i].y = radius * sin(ang);
|
||||
}
|
||||
int k = 5; // No of clusters
|
||||
cluster* clusters = kMeans(observations, size, k);
|
||||
printEPS(observations, size, clusters, k);
|
||||
// Free the accquired memory
|
||||
free(observations);
|
||||
free(clusters);
|
||||
}
|
||||
|
||||
/*! @fn test2
|
||||
/*!
|
||||
* A function to test the kMeans function
|
||||
* Generates 1000000 points in a circle of
|
||||
* radius 20.0 with center at (0,0)
|
||||
|
|
@ -301,35 +351,40 @@ static void test() {
|
|||
* <img alt="Output for 1000000 points divided in 11 clusters" src=
|
||||
* "https://raw.githubusercontent.com/TheAlgorithms/C/docs/images/machine_learning/k_means_clustering/kMeansTest2.png"
|
||||
* width="400px" heiggt="400px">
|
||||
* @returns None
|
||||
*/
|
||||
void test2() {
|
||||
size_t size = 1000000L;
|
||||
observation* observations = (observation*)malloc(sizeof(observation) * size);
|
||||
double maxRadius = 20.00;
|
||||
double radius = 0;
|
||||
double ang = 0;
|
||||
size_t i = 0;
|
||||
for (; i < size; i++) {
|
||||
radius = maxRadius * ((double)rand() / RAND_MAX);
|
||||
ang = 2 * M_PI * ((double)rand() / RAND_MAX);
|
||||
observations[i].x = radius * cos(ang);
|
||||
observations[i].y = radius * sin(ang);
|
||||
}
|
||||
int k = 11; // No of clusters
|
||||
cluster* clusters = kMeans(observations, size, k);
|
||||
printEPS(observations, size, clusters, k);
|
||||
// Free the accquired memory
|
||||
free(observations);
|
||||
free(clusters);
|
||||
void test2()
|
||||
{
|
||||
size_t size = 1000000L;
|
||||
observation* observations =
|
||||
(observation*)malloc(sizeof(observation) * size);
|
||||
double maxRadius = 20.00;
|
||||
double radius = 0;
|
||||
double ang = 0;
|
||||
size_t i = 0;
|
||||
for (; i < size; i++)
|
||||
{
|
||||
radius = maxRadius * ((double)rand() / RAND_MAX);
|
||||
ang = 2 * M_PI * ((double)rand() / RAND_MAX);
|
||||
observations[i].x = radius * cos(ang);
|
||||
observations[i].y = radius * sin(ang);
|
||||
}
|
||||
int k = 11; // No of clusters
|
||||
cluster* clusters = kMeans(observations, size, k);
|
||||
printEPS(observations, size, clusters, k);
|
||||
// Free the accquired memory
|
||||
free(observations);
|
||||
free(clusters);
|
||||
}
|
||||
|
||||
/*! @fn main
|
||||
/*!
|
||||
* This function calls the test
|
||||
* function
|
||||
*/
|
||||
int main() {
|
||||
srand(time(NULL));
|
||||
test();
|
||||
/* test2(); */
|
||||
return 0;
|
||||
int main()
|
||||
{
|
||||
srand(time(NULL));
|
||||
test();
|
||||
/* test2(); */
|
||||
return 0;
|
||||
}
|
||||
|
|
|
|||
Loading…
Reference in New Issue