def __init__(self, ci, cn, transf, max_iter, delta):
Layer.__init__(self, ci, cn, cn, {'w': (cn, ci), 'b': cn})
self.max_iter = max_iter
self.delta = delta
self.transf = transf
self.outs = []
if not hasattr(transf, 'out_minmax'):
test = np.asfarry([-1e100, -100, -10, -1, 0, 1, 10, 100, 1e100])
val = self.transf(test)
self.out_minmax = np.array([val.min(), val.max()] * self.co)
else:
self.out_minmax = np.asfarray([transf.out_minmax] * self.co)
self.initf = None
self.s = np.zeros(self.cn)
def __init__(self, ci, cn, transf):
Layer.__init__(self, ci, cn, cn, {'w':(cn, ci), 'b': cn})
self.transf = transf
if not hasattr(transf, 'out_minmax'):
Inf = 1e100
self.out_minmax = np.array([(self.transf(-Inf), self.transf(Inf))] * self.co)
else:
self.out_minmax = np.array([np.asfarray(transf.out_minmax)] * self.co)
# default init function
self.initf = init.initwb_reg
self.s = np.zeros(self.cn)
def __init__(self, ci, cn, transf, max_iter, delta):
Layer.__init__(self, ci, cn, cn, {'w': (cn, ci), 'b': cn})
self.max_iter = max_iter
self.delta = delta
self.transf = transf
self.outs = []
if not hasattr(transf, 'out_minmax'):
test = np.asfarry([-1e100, -100, -10, -1, 0, 1, 10, 100, 1e100])
val = self.transf(test)
self.out_minmax = np.array([val.min(), val.max()] * self.co)
else:
self.out_minmax = np.asfarray([transf.out_minmax] * self.co)
self.initf = None
self.s = np.zeros(self.cn)
def __init__(self, ci, cn, transf):
Layer.__init__(self, ci, cn, cn, {'w': (cn, ci), 'b': cn})
self.transf = transf
if not hasattr(transf, 'out_minmax'):
test = np.asfarry([-1e100, -100, -10, -1, 0, 1, 10, 100, 1e100])
val = self.transf(test)
self.out_minmax = np.array([val.min(), val.max()] * self.co)
else:
self.out_minmax = np.asfarray([transf.out_minmax] * self.co)
# default init function
self.initf = init.initwb_reg
#self.initf = init.initwb_nw
self.s = np.zeros(self.cn)
def __init__(self, ci, cn, transf):
Layer.__init__(self, ci, cn, cn, {'w': (cn, ci), 'b': cn})
self.transf = transf
if not hasattr(transf, 'out_minmax'):
test = np.asfarry([-1e100, -100, -10, -1, 0, 1, 10, 100, 1e100])
val = self.transf(test)
self.out_minmax = np.array([val.min(), val.max()] * self.co)
else:
self.out_minmax = np.asfarray([transf.out_minmax] * self.co)
# default init function
self.initf = init.initwb_reg
#self.initf = init.initwb_nw
self.s = np.zeros(self.cn)
def __init__(self, ci, cn, distf=None):
Layer.__init__(self, ci, cn, cn, {'w': (cn, ci), 'conscience': cn})
self.transf = trans.Competitive()
self.initf = init.midpoint
self.out_minmax[:] = np.array([self.transf.out_minmax] * cn)
self.np['conscience'].fill(1.0)
def euclidean(A, B):
"""
Euclidean distance function.
See scipi.spatial.distance.cdist()
:Example:
>>> import numpy as np
>>> euclidean(np.array([0,0]), np.array([[0,1], [0, 5.5]])).tolist()
[1.0, 5.5]
"""
return np.sqrt(np.sum(np.square(A-B) ,axis=1))
self.distf = euclidean
def __init__(self, ci, cn, distf=None):
Layer.__init__(self, ci, cn, cn, {'w': (cn, ci), 'conscience': cn})
self.transf = trans.Competitive()
self.initf = init.midpoint
self.out_minmax[:] = np.array([self.transf.out_minmax] * cn)
self.np['conscience'].fill(1.0)
def euclidean(A, B):
"""
Euclidean distance function.
See scipi.spatial.distance.cdist()
:Example:
>>> import numpy as np
>>> euclidean(np.array([0,0]), np.array([[0,1], [0, 5.5]])).tolist()
[1.0, 5.5]
"""
return np.sqrt(np.sum(np.square(A-B) ,axis=1))
self.distf = euclidean
def __init__(self, ci, cb, transf, cbo=1, shift=1):
Layer.__init__(self, ci, cn, cn, {'w': (cn, cb), 'b': cn})
self.cb = cb
self.shift = shift
self.transf = transf
