dist vs sim adjusted
@@ -55,8 +55,8 @@ def compare_cosine(rdm1, rdm2):
"""
vector1, vector2 = _parse_input_rdms(rdm1, rdm2)
- dist = _cosine(vector1, vector2)
- return 1 - dist
+ sim = _cosine(vector1, vector2)
+ return 1 - sim
def compare_correlation(rdm1, rdm2):
@@ -75,8 +75,8 @@ def compare_correlation(rdm1, rdm2):
vector1, vector2 = _parse_input_rdms(rdm1, rdm2)
vector1 = vector1 - np.mean(vector1, 1, keepdims=True)
vector2 = vector2 - np.mean(vector2, 1, keepdims=True)
- dist = _cosine(vector1, vector2)
- return 1 - dist
+ sim = _cosine(vector1, vector2)
+ return 1 - sim
def compare_rank_corr(rdm1, rdm2):
@@ -93,8 +93,8 @@ def compare_rank_corr(rdm1, rdm2):
"""
vector1, vector2 = _parse_input_rdms(rdm1, rdm2)
- dist = _all_combinations(vector1, vector2, _spearman_r)
- return 1 - dist
+ sim = _all_combinations(vector1, vector2, _spearman_r)
+ return 1 - sim
def compare_kendall_tau(rdm1, rdm2):
@@ -112,8 +112,8 @@ def compare_kendall_tau(rdm1, rdm2):
kendall-tau based distance between the two RDMs
"""
vector1, vector2 = _parse_input_rdms(rdm1, rdm2)
- dist = _all_combinations(vector1, vector2, _kendall_tau)
- return 1 - dist
+ sim = _all_combinations(vector1, vector2, _kendall_tau)
+ return 1 - sim
def _all_combinations(vectors1, vectors2, func):
