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@@ -63,22 +63,10 @@ def create_distance_matrix(dense_data):
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matrix = [[ None for i in range(size)] for j in range(size)]
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- # Calculate the distances. As the distance matrix is symmetric we can do
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- # that with at most n*n/2 distance evaluations.
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+ # Calculate the squared euclidian distances.
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for nx, x in enumerate(dense_data):
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- for ny, y in enumerate(dense_data):
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- # Same index so distance is 0.0
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- if nx == ny:
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- matrix[nx][ny] = 0.0
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-
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- # As the matrix is symetric we can copy already calculated values.
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- # As distance computation is expencive we should do that.
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- elif nx > ny:
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- matrix[nx][ny] = matrix[ny][nx]
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-
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- # Unseen pair so calculate the distance.
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- else:
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- matrix[nx][ny] = distance.euclidean(x, y)
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+ b = dense_data - x
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+ matrix[nx] = np.sum(b*b, axis=1)
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# Calculate the indices and replace the distance rows.
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# So we create our result matrix and do cleanup at the same time.
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