def k_means(t, nbclusters=2, nbiter=3, medoids=False, soft=False, beta=200.0,\
        distance=lambda x,y: math.sqrt(np.dot(x-y,(x-y).conj())),\
        responsability=lambda beta,d: math.exp(-1 * beta * d)):
    """ 
    Each row of t is an observation, each column is a feature 
    'nbclusters' is the number of seeds and so of clusters/centroids 
    'nbiter' is the number of iterations
    'medoids' tells if we use the medoids or centroids method
    'distance' is the function to use for comparing observations

    Overview of the algorithm ("hard k-means"):
    -> Place nbclusters points into the features space of the objects/t[i:]
    -> Assign each object to the group that has the closest centroid (distance)
    -> Recalculate the positions of the nbclusters centroids
    -> Repeat Steps 2 and 3 until the centroids no longer move

    We can change the distance function and change the responsability function
    -> distance will change the shape of the clusters
    -> responsability will change the breadth of the clusters (& associativity)
    """
    nbobs = t.shape[0]
    nbfeatures = t.shape[1]
    # find xranges for each features
    min_max = []
    for f in xrange(nbfeatures):
        min_max.append((t[:,f].min(), t[:,f].max()))

    ### Soft => Normalization, otherwise "beta" has no meaning!
    if soft:
        for f in xrange(nbfeatures):
            t[:,f] -= min_max[f][0]
            t[:,f] /= (min_max[f][1]-min_max[f][0])
    min_max = []
    for f in xrange(nbfeatures):
        min_max.append((t[:,f].min(), t[:,f].max()))
    ### /Normalization # ugly

    result = {}
    quality = 0.0 # sum of the means of the distances to centroids
    random.seed()
    tmpdist = np.ndarray([nbobs,nbclusters], np.float64) # distance obs<->clust
    tmpresp = np.ndarray([nbobs,nbclusters], np.float64) # responsability o<->c
    # iterate for the best quality
    for iteration in xrange(nbiter):
        clusters = [[] for c in xrange(nbclusters)]
        # Step 1: place nbclusters seeds for each features
        centroids = [np.array([random.uniform(min_max[f][0], min_max[f][1])\
                for f in xrange(nbfeatures)], np.float64)\
                for c in xrange(nbclusters)]
        old_centroids = [np.array([-1 for f in xrange(nbfeatures)], np.float64)\
                for c in xrange(nbclusters)] # should not be init, TODO
        new_sum = math.fsum([distance(centroids[c], old_centroids[c])\
                for c in xrange(nbclusters)])
        old_sum = sys.maxint
        np.seterr(invalid='raise')
        # iterate until convergence
        while new_sum < old_sum :
            old_centroids = copy.deepcopy(centroids)
            old_sum = new_sum
            for c in xrange(nbclusters):
                clusters[c] = []
            # precompute distance to all centroids/medoids for all observations
            for c in xrange(nbclusters):
                for o in xrange(nbobs):
                    tmpdist[o,c] = distance(centroids[c], t[o,:])
            if soft:
                # Step 2: compute the degree of assignment for each object
                for o in xrange(nbobs):
                    for c in xrange(nbclusters):
                        tmpresp[o,c] = responsability(beta, tmpdist[o,c])
                for o in xrange(nbobs):
                    tmpresp[o,:] /= math.fsum(tmpresp[o,:])
            else:
                # Step 2: assign each object to the closest centroid
                for o in xrange(nbobs):
                    clusters[tmpdist[o,:].argmin()].append(o)
            # Step 3: recalculate the positions of the nbclusters centroids
            for c in xrange(nbclusters):
                if medoids:
                    if soft:
                        print "ERROR: Soft medoids not implemented"
                        sys.exit(-1)
                    else:
                        tmpmin = sys.maxint
                        argmin = 0
                        for o in clusters[c]:
                            if tmpdist[o,c] < tmpmin:
                                tmpmin = tmpdist[o,c]
                                argmin = o
                        centroids[c] = t[argmin,:]
                else:
                    mean = np.array([0 for i in xrange(nbfeatures)], np.float64)
                    if soft:
                        for o in xrange(nbobs):
                            mean += tmpresp[o,c] * t[o,:]
                        mean /= math.fsum(tmpresp[:,c])
                    else:
                        for o in clusters[c]:
                            mean += t[o,:]
                        mean = map(lambda x: x/len(clusters[c]), mean)
                    centroids[c] = np.array(mean, np.float64)
            print centroids
            new_sum = math.fsum([distance(centroids[c], old_centroids[c])\
                    for c in xrange(nbclusters)])
            print "(k-means) old and new sum: ", old_sum, new_sum
        if soft:
            for o in xrange(nbobs):
                clusters[tmpdist[o,:].argmin()].append(o)
        quality = math.fsum([math.fsum([tmpdist[o][c] for o in clusters[c]])\
                /(len(clusters[c])+1) for c in xrange(nbclusters)])
        if not quality in result or quality > result['quality']:
            result['quality'] = quality
            result['centroids'] = centroids
            result['clusters'] = clusters
    return result
 
    Each row of t is an observation, each column is a feature 
    'nbclusters' is the number of seeds and so of clusters/centroids 
    'nbiter' is the number of iterations
    'medoids' tells if we use the medoids or centroids method
    'distance' is the function to use for comparing observations

    Overview of the algorithm ("hard k-means"):
    -> Place nbclusters points into the features space of the objects/t[i:]
    -> Assign each object to the group that has the closest centroid (distance)
    -> Recalculate the positions of the nbclusters centroids
    -> Repeat Steps 2 and 3 until the centroids no longer move

    We can change the distance function and change the responsability function
    -> distance will change the shape of the clusters
    -> responsability will change the breadth of the clusters (& associativity)
    
recipes/Python/577734_Kmeans/recipe-577734.py