comparison Marea/marea_cluster.py @ 31:944e15aa970a draft

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30:e88efefbd015

    range_n_clusters = [i for i in range(k_min, k_max+1)]
    distortions = []
    scores = []
    all_labels = []
    
    for n_clusters in range_n_clusters:
        clusterer = KMeans(n_clusters=n_clusters, random_state=10)
        cluster_labels = clusterer.fit_predict(dataset)
        
        all_labels.append(cluster_labels)

############################## elbow_plot ####################################
    
def elbow_plot (distortions, k_min, k_max):
    plt.figure(0)
    plt.plot(range(k_min, k_max+1), distortions, marker = 'o')
    plt.xlabel('Number of cluster')
    plt.ylabel('Distortion')
    s = 'clustering/elbow_plot.png'
    fig = plt.gcf()
    fig.set_size_inches(18.5, 10.5, forward = True)
    fig.savefig(s, dpi=100)

31:944e15aa970a

    range_n_clusters = [i for i in range(k_min, k_max+1)]
    distortions = []
    scores = []
    all_labels = []
    
    clusterer = KMeans(n_clusters=1, random_state=10)
    distortions.append(clusterer.fit(dataset).inertia_)
    
    
    for n_clusters in range_n_clusters:
        clusterer = KMeans(n_clusters=n_clusters, random_state=10)
        cluster_labels = clusterer.fit_predict(dataset)
        
        all_labels.append(cluster_labels)

############################## elbow_plot ####################################
    
def elbow_plot (distortions, k_min, k_max):
    plt.figure(0)
    x = list(range(k_min, k_max + 1))
    x.insert(0, 1)
    plt.plot(x, distortions, marker = 'o')
    plt.xlabel('Number of clusters (k)')
    plt.ylabel('Distortion')
    s = 'clustering/elbow_plot.png'
    fig = plt.gcf()
    fig.set_size_inches(18.5, 10.5, forward = True)
    fig.savefig(s, dpi=100)