Resolve "Add plots"

Closes #5 (closed)

code/bolsonaro/visualization/plotter.py

 import matplotlib.pyplot as plt
 import numpy as np
-from sklearn.neighbors.kde import KernelDensity
+import pandas as pd
 
 
 class Plotter(object):
 
     @staticmethod
-    def weight_density(weights, X, file_path):
-        X_plot = [np.exp(elem) for elem in weights]
-        fig, ax = plt.subplots()
+    def weight_density(all_experiment_weights, file_path):
+        '''
+        Function that creates the figure with the density of the weights
+        :param all_experiment_weights: The weights for the different experiments
+        :param file path: str, path where the figure will be saved
+        '''
 
-        for kernel in ['gaussian', 'tophat', 'epanechnikov']:
-            kde = KernelDensity(kernel=kernel, bandwidth=0.5).fit(X_plot)
-            log_dens = kde.score_samples(X_plot)
-            ax.plot(X_plot, np.exp(log_dens), '-',
-                    label="kernel = '{0}'".format(kernel))
+        all_experiment_weights = np.array(list(all_experiment_weights.values()))
+        n = len(all_experiment_weights)
+        colors = Plotter.get_colors_from_cmap(n)
 
-        ax.legend(loc='upper left')
-        ax.plot(X[:, 0], -0.005 - 0.01 * np.random.random(X.shape[0]), '+k')
+        fig, ax = plt.subplots()
+        for i in range(n):
+            for weights in all_experiment_weights[i]:
+                pd.Series([weight for weight in weights if weight != 0]).plot.kde(
+                    figsize=(15, 10), ax=ax, color=colors[i])
 
-        ax.set_xlim(-4, 9)
-        ax.set_ylim(-0.02, 0.4)
+        ax.set_title('Density weights of the OMP')
         fig.savefig(file_path, dpi=fig.dpi)
         plt.close(fig)