def _compute_vectorized(self, s_left, s_right):
d = _1d_distance(s_left, s_right)
if self.method == 'step':
num_sim_alg = partial(_step_sim, d, self.offset, self.origin)
elif self.method in ['linear', 'lin']:
num_sim_alg = partial(_linear_sim, d, self.scale, self.offset,
self.origin)
elif self.method == 'squared':
num_sim_alg = partial(_squared_sim, d, self.scale, self.offset,
self.origin)
elif self.method in ['exp', 'exponential']:
num_sim_alg = partial(_exp_sim, d, self.scale, self.offset,
self.origin)
elif self.method in ['gauss', 'gaussian']:
num_sim_alg = partial(_gauss_sim, d, self.scale, self.offset,
self.origin)
else:
raise ValueError("The algorithm '{}' is not known.".format(
self.method))
c = num_sim_alg()
c = _fillna(c, self.missing_value)
return c
def _num_internal(s1, s2, call_method, *args, **kwargs):
"""Internal function to compute the numeric similarity."""
# compute the 1D distance between the values
d = _1d_distance(s1, s2)
return call_method(d, *args, **kwargs)
def _num_internal(s1, s2, method, *args, **kwargs):
"""
Internal function to compute the numeric similarity algorithms.
"""
# compute the 1D distance between the values
d = _1d_distance(s1, s2)
if method == 'step':
num_sim_alg = _step_sim
elif method in ['linear', 'lin']:
num_sim_alg = _linear_sim
elif method == 'squared':
num_sim_alg = _squared_sim
elif method in ['exp', 'exponential']:
num_sim_alg = _exp_sim
elif method in ['gauss', 'gaussian']:
num_sim_alg = _gauss_sim
else:
raise ValueError("The algorithm '{}' is not known.".format(method))
return num_sim_alg(d, *args, **kwargs)
def _num_internal(s1, s2, method, *args, **kwargs):
"""
Internal function to compute the numeric similarity algorithms.
"""
# compute the 1D distance between the values
d = _1d_distance(s1, s2)
if method == 'step':
num_sim_alg = _step_sim
elif method in ['linear', 'lin']:
num_sim_alg = _linear_sim
elif method == 'squared':
num_sim_alg = _squared_sim
elif method in ['exp', 'exponential']:
num_sim_alg = _exp_sim
elif method in ['gauss', 'gaussian']:
num_sim_alg = _gauss_sim
else:
raise ValueError("The algorithm '{}' is not known.".format(method))
return num_sim_alg(d, *args, **kwargs)
