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__init__(self,
k,
r=0.001,
svd=False,
verbose=False,
input_dim=None,
output_dim=None,
dtype=None)
:Arguments:
k
number of nearest neighbors to use
r
regularization constant; if ``None``, ``r`` is automatically
computed using the method presented in deRidder and Duin;
this method involves solving an eigenvalue problem for
every data point, and can slow down the algorithm
If specified, it multiplies the trace of the local covariance
matrix of the distances, as in Saul & Roweis (faster)
svd
if true, use SVD to compute the projection matrix;
SVD is slower but more stable
verbose
if true, displays information about the progress
of the algorithm
output_dim
number of dimensions to output or a float between 0.0 and
1.0. |
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_stop_training(self)
Concatenate the collected data in a single array. |
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execute(self,
x)
Process the data contained in `x`. |
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stop_training(self)
Concatenate the collected data in a single array. |
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Inherited from unreachable.newobject :
__long__ ,
__native__ ,
__nonzero__ ,
__unicode__ ,
next
Inherited from object :
__delattr__ ,
__format__ ,
__getattribute__ ,
__hash__ ,
__new__ ,
__reduce__ ,
__reduce_ex__ ,
__setattr__ ,
__sizeof__ ,
__subclasshook__
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_train(self,
*args)
Collect all input data in a list. |
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train(self,
*args)
Collect all input data in a list. |
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__call__(self,
x,
*args,
**kwargs)
Calling an instance of `Node` is equivalent to calling
its `execute` method. |
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_refcast(self,
x)
Helper function to cast arrays to the internal dtype. |
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copy(self,
protocol=None)
Return a deep copy of the node. |
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inverse(self,
y,
*args,
**kwargs)
Invert `y`. |
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is_training(self)
Return True if the node is in the training phase,
False otherwise. |
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save(self,
filename,
protocol=-1)
Save a pickled serialization of the node to `filename`. |
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set_dtype(self,
t)
Set internal structures' dtype. |
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