Delete metrics_sortem.py

metrics_sortem.py

-  
-import numpy as np
-
-
-nmi = normalized_mutual_info_score
-ari = adjusted_rand_score
-
-def cos_grad(grad1, grad2):
-    grad1_list = []
-    grad2_list = []
-    for i in range(len(grad1)):
-        grad1_list.append(grad1[i][0].flatten())
-        grad2_list.append(grad2[i][0].flatten())
-    grad1_vector = np.concatenate(grad1_list)
-    grad2_vector = np.concatenate(grad2_list)
-    return np.matmul(grad1_vector, grad2_vector) / ((np.linalg.norm(grad1_vector)) * (np.linalg.norm(grad2_vector)))
-
-def acc(y_true, y_pred):
-    """
-    Calculate clustering accuracy. Require scikit-learn installed
-    # Arguments
-        y: true labels, numpy.array with shape `(n_samples,)`
-        y_pred: predicted labels, numpy.array with shape `(n_samples,)`
-    # Return
-        accuracy, in [0,1]
-    """
- 
-    assert y_pred.size == y_true.size
-    D = max(y_pred.max(), y_true.max()) + 1
-    w = np.zeros((D, D), dtype=np.int64)
-    for i in range(y_pred.size):
-        w[y_pred[i], y_true[i]] += 1
-    #from sklearn.utils.linear_assignment_ import linear_assignment
-    from scipy.optimize import linear_sum_assignment
-    row_ind, col_ind = linear_sum_assignment(w.max() - w)
-    return sum([w[i, j] for i, j in zip(row_ind, col_ind)]) * 1.0 / y_pred.size
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