import numpy as np
import pandas

from sklearn.cluster import DBSCAN
from sklearn import metrics
from sklearn.datasets.samples_generator import make_blobs
from sklearn.preprocessing import StandardScaler

def cluster(file_in, file_out):
    # #############################################################################
    data_df = pandas.read_csv("values_fromhtml_GV12.csv", sep=",")
    data_df.head(3)
    data = data_df[["X1","Y1","X2","Y2"]]
    print(data)
    data = StandardScaler().fit_transform(data)

    # #############################################################################
    # Compute DBSCAN
    db = DBSCAN(eps=0.2, min_samples=1).fit(data)
    core_samples_mask = np.zeros_like(db.labels_, dtype=bool)
    core_samples_mask[db.core_sample_indices_] = True
    labels = db.labels_
    print(data[labels == 0])
    data_df["cluster"] = labels

    # Number of clusters in labels, ignoring noise if present.
    n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)
    n_noise_ = list(labels).count(-1)

    print('Estimated number of clusters: %d' % n_clusters_)
    print('Estimated number of noise points: %d' % n_noise_)
    print("Silhouette Coefficient: %0.3f"
          % metrics.silhouette_score(data, labels))

    # #############################################################################
    # Plot result
    """ort matplotlib.pyplot as plt

    # Black removed and is used for noise instead.
    unique_labels = set(labels)
    colors = [plt.cm.Spectral(each)
              for each in np.linspace(0, 1, len(unique_labels))]
    for k, col in zip(unique_labels, colors):
        if k == -1:
            # Black used for noise.
            col = [0, 0, 0, 1]

        class_member_mask = (labels == k)

        xy = data[class_member_mask & core_samples_mask]
        plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=tuple(col),
                 markeredgecolor='k', markersize=14)

        xy = data[class_member_mask & ~core_samples_mask]
        plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=tuple(col),
                 markeredgecolor='k', markersize=6)

    plt.title('Estimated number of clusters: %d' % n_clusters_)
    plt.show()"""

    print(data_df.head(3))
    #data_df.to_csv("values_clusteredfromPDF_GV12.csv")
    data_df.groupby('cluster')['Text'].apply(' '.join).reset_index().to_csv("values_clusteredfromPDF_GV12.csv")


cluster(33,33)