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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def get_diversity(self,X):
 """compute mean diversity of individual outputs"""

        # diversity in terms of cosine distances between features
        feature_correlations = np.zeros(X.shape[0]-1)
        for i in np.arange(1,X.shape[0]-1):
            feature_correlations[i] = max(0.0,r2_score(X[0],X[i]))
        # pdb.set_trace()
        self.diversity.append(1-np.mean(feature_correlations))