Code snippets for page NodesΒΆ
Download nodes.py.
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# -*- coding: utf-8 -*-
# Generated by codesnippet sphinx extension on 2020-12-16
import mdp
import numpy as np
np.random.seed(0)
pcanode1 = mdp.nodes.PCANode()
pcanode1
# Expected:
## PCANode(input_dim=None, output_dim=None, dtype=None)
pcanode2 = mdp.nodes.PCANode(output_dim=10)
pcanode2
# Expected:
## PCANode(input_dim=None, output_dim=10, dtype=None)
pcanode3 = mdp.nodes.PCANode(output_dim=0.8)
pcanode3.desired_variance
# Expected:
## 0.8
pcanode4 = mdp.nodes.PCANode(dtype='float32')
pcanode4
# Expected:
## PCANode(input_dim=None, output_dim=None, dtype='float32')
pcanode4.supported_dtypes
# Expected:
## [dtype('float32'), dtype('float64')...]
expnode = mdp.nodes.PolynomialExpansionNode(3)
x = np.random.random((100, 25)) # 25 variables, 100 observations
pcanode1.train(x)
pcanode1
# Expected:
## PCANode(input_dim=25, output_dim=None, dtype='float64')
for i in range(100):
x = np.random.random((100, 25))
pcanode1.train(x)
expnode.is_trainable()
# Expected:
## False
pcanode1.stop_training()
pcanode3.train(x)
pcanode3.stop_training()
pcanode3.output_dim
# Expected:
## 16
pcanode3.explained_variance
# Expected:
## 0.85261144755506446
avg = pcanode1.avg # mean of the input data
v = pcanode1.get_projmatrix() # projection matrix
fdanode = mdp.nodes.FDANode()
for label in ['a', 'b', 'c']:
x = np.random.random((100, 25))
fdanode.train(x, label)
fdanode.stop_training()
for label in ['a', 'b', 'c']:
x = np.random.random((100, 25))
fdanode.train(x, label)
x = np.random.random((100, 25))
y_pca = pcanode1.execute(x)
y_pca = pcanode1(x)
x = np.random.random((100, 5))
y_exp = expnode(x)
x = np.random.random((100, 25))
y_fda = fdanode(x)
pcanode1.is_invertible()
# Expected:
## True
x = pcanode1.inverse(y_pca)
expnode.is_invertible()
# Expected:
## False
class TimesTwoNode(mdp.Node):
def is_trainable(self):
return False
def _execute(self, x):
return 2*x
def _inverse(self, y):
return y/2
node = TimesTwoNode(dtype = 'float32')
x = mdp.numx.array([[1.0, 2.0, 3.0]])
y = node(x)
print x, '* 2 = ', y
# Expected:
## [[ 1. 2. 3.]] * 2 = [[ 2. 4. 6.]]
print y, '/ 2 =', node.inverse(y)
# Expected:
## [[ 2. 4. 6.]] / 2 = [[ 1. 2. 3.]]
class PowerNode(mdp.Node):
def __init__(self, power, input_dim=None, dtype=None):
super(PowerNode, self).__init__(input_dim=input_dim, dtype=dtype)
self.power = power
def is_trainable(self):
return False
def is_invertible(self):
return False
def _get_supported_dtypes(self):
return ['float32', 'float64']
def _execute(self, x):
return self._refcast(x**self.power)
node = PowerNode(3)
x = mdp.numx.array([[1.0, 2.0, 3.0]])
y = node(x)
print x, '**', node.power, '=', node(x)
# Expected:
## [[ 1. 2. 3.]] ** 3 = [[ 1. 8. 27.]]
class MeanFreeNode(mdp.Node):
def __init__(self, input_dim=None, dtype=None):
super(MeanFreeNode, self).__init__(input_dim=input_dim,
dtype=dtype)
self.avg = None
self.tlen = 0
def _train(self, x):
# Initialize the mean vector with the right
# size and dtype if necessary:
if self.avg is None:
self.avg = mdp.numx.zeros(self.input_dim,
dtype=self.dtype)
self.avg += mdp.numx.sum(x, axis=0)
self.tlen += x.shape[0]
def _stop_training(self):
self.avg /= self.tlen
if self.output_dim is None:
self.output_dim = self.input_dim
def _execute(self, x):
return x - self.avg
def _inverse(self, y):
return y + self.avg
node = MeanFreeNode()
x = np.random.random((10,4))
node.train(x)
y = node(x)
print 'Mean of y (should be zero):\n', np.abs(np.around(np.mean(y, 0), 15))
# Expected:
## Mean of y (should be zero):
## [ 0. 0. 0. 0.]
class UnitVarianceNode(mdp.Node):
def __init__(self, input_dim=None, dtype=None):
super(UnitVarianceNode, self).__init__(input_dim=input_dim,
dtype=dtype)
self.avg = None # average
self.std = None # standard deviation
self.tlen = 0
def _get_train_seq(self):
return [(self._train_mean, self._stop_mean),
(self._train_std, self._stop_std)]
def _train_mean(self, x):
if self.avg is None:
self.avg = mdp.numx.zeros(self.input_dim,
dtype=self.dtype)
self.avg += mdp.numx.sum(x, 0)
self.tlen += x.shape[0]
def _stop_mean(self):
self.avg /= self.tlen
def _train_std(self, x):
if self.std is None:
self.tlen = 0
self.std = mdp.numx.zeros(self.input_dim,
dtype=self.dtype)
self.std += mdp.numx.sum((x - self.avg)**2., 0)
self.tlen += x.shape[0]
def _stop_std(self):
# compute the standard deviation
self.std = mdp.numx.sqrt(self.std/(self.tlen-1))
def _execute(self, x):
return (x - self.avg)/self.std
def _inverse(self, y):
return y*self.std + self.avg
node = UnitVarianceNode()
x = np.random.random((10,4))
# loop over phases
for phase in range(2):
node.train(x)
node.stop_training()
# execute
y = node(x)
print 'Standard deviation of y (should be one): ', mdp.numx.std(y, axis=0, ddof=1)
# Expected:
## Standard deviation of y (should be one): [ 1. 1. 1. 1.]
class TwiceNode(mdp.Node):
def is_trainable(self): return False
def is_invertible(self): return False
def _set_input_dim(self, n):
self._input_dim = n
self._output_dim = 2*n
def _set_output_dim(self, n):
raise mdp.NodeException, "Output dim can not be set explicitly!"
def _execute(self, x):
return mdp.numx.concatenate((x, x), 1)
node = TwiceNode()
x = mdp.numx.zeros((5,2))
x
# Expected:
## array([[ 0., 0.],
## [ 0., 0.],
## [ 0., 0.],
## [ 0., 0.],
## [ 0., 0.]])
node.execute(x)
# Expected:
## array([[ 0., 0., 0., 0.],
## [ 0., 0., 0., 0.],
## [ 0., 0., 0., 0.],
## [ 0., 0., 0., 0.],
## [ 0., 0., 0., 0.]])
