Commit 210888a9 authored by Jonathan Juhl's avatar Jonathan Juhl
Browse files


parent 87f24eea
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_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
\ No newline at end of file
Supports Markdown
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment