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Repository: jfpower/anfis-pytorch
Branch: master
Commit: 060acad8f14b
Files: 30
Total size: 2.4 MB
Directory structure:
gitextract_n4pn8s9r/
├── .gitignore
├── LICENSE
├── README.md
├── anfis.py
├── cluster_data/
│ ├── Aggregation.txt
│ ├── D31.txt
│ ├── R15.txt
│ ├── a3.txt
│ ├── birch3.txt
│ └── jain.txt
├── cmeans.py
├── experimental.py
├── fileio/
│ ├── EvaluateXML.java
│ ├── astext.py
│ ├── fcl.py
│ ├── test-model.txt
│ ├── test_astext.py
│ ├── test_fcl.py
│ ├── test_jfml_out.xml
│ ├── test_tojfml.py
│ └── tojfml.py
├── iris_example.py
├── jang-example4-data.chk
├── jang-example4-data.trn
├── jang_examples.py
├── jang_inverse_example.py
├── jang_pendulum_example.py
├── membership.py
├── sk_examples.py
└── vignette_examples.py
================================================
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================================================
FILE: LICENSE
================================================
MIT License
Copyright (c) 2019 James F. Power
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
================================================
FILE: README.md
================================================
# ANFIS in pyTorch #
This is an implementation of the ANFIS system using pyTorch.
### ANFIS
ANFIS is a way of presenting a fuzzy inference system (FIS) as a
series of numeric layers so that it can be trained like a neural net.
The canonical reference is the original paper by
[Jyh-Shing Roger Jang](http://mirlab.org/jang/):
* Jang, J.-S.R. (1993). "ANFIS: adaptive-network-based fuzzy inference
system". IEEE Transactions on Systems, Man and Cybernetics. 23 (3):
665–685. doi:10.1109/21.256541
Note that it assumes a Takagi Sugeno Kang (TSK) style of
defuzzification rather than the more usual Mamdani style.
### Background: other implementations
The original C code from Jang that implements the ANFIS system, along
with is test cases, is available from
[a repository at CMU](https://www.cs.cmu.edu/Groups/AI/areas/fuzzy/systems/anfis/).
The version most people seem to use is the
[ANFIS library for Matlab](https://www.mathworks.com/help/fuzzy/anfis.html).
Their [documentation](https://www.mathworks.com/help/fuzzy/neuro-adaptive-learning-and-anfis.html) is quite helpful for understanding how ANFIS works,
even if you don't use Matlab.
There's an implementation for the R language by Cristobal Fresno and Elmer
A. Fernandez of the
[BioScience Data Mining Group](http://www.bdmg.com.ar/?page_id=176)
in Argentina (that URL seems a bit unstable).
Again, their documentation is very helpful, particularly
the "ANFIS vignette" report that comes with the distribution (I've put a
[local copy](./Anfis-vignette.pdf) here). It
shows how to run the system using examples from Jang's paper, and gives
some of the results.
I also found a re-implementation of this R code in Python
[anfis](https://github.com/twmeggs/anfis) by Tim Meggs that was helpful
in understanding the original R code.
### Navigation
The ANFIS framework is mainly in three files:
* [anfis.py](./anfis.py) This is where the layers of the ANFIS system
are defined as Torch modules.
* [membership.py](./membership.py) At the moment I only have Bell and
Gaussian membership functions, but any others will go in here too.
* [experimental.py](./experimental.py) The experimental infrastructure
to train and test the FIS, and to plot some graphs etc.
There are then some runnable examples:
* [jang_examples.py](./jang_examples.py) these are four
examples from Jang's paper (based partly on the details in the
paper, and particle on the example folders in his source code
distribution).
* [vignette_examples.py](./vignette_examples.py) these are
three examples from the Vignette paper. Two of these use Gaussians
rather than Bell MFs.
### Installation
You need to install Python and PyTorch, nothing special.
I'm using
[Python 3.6.5](https://www.python.org/downloads/),
the [Anaconda 4.6.11](https://www.anaconda.com/distribution/) distribution
and [PyTorch](https://pytorch.org) version 1.0.1.
### Author ###
* [James Power](http://www.cs.nuim.ie/~jpower/), Maynooth University.
================================================
FILE: anfis.py
================================================
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
'''
ANFIS in torch: the ANFIS layers
@author: James Power <james.power@mu.ie> Apr 12 18:13:10 2019
Acknowledgement: twmeggs' implementation of ANFIS in Python was very
useful in understanding how the ANFIS structures could be interpreted:
https://github.com/twmeggs/anfis
'''
import itertools
from collections import OrderedDict
import numpy as np
import torch
import torch.nn.functional as F
dtype = torch.float
class FuzzifyVariable(torch.nn.Module):
'''
Represents a single fuzzy variable, holds a list of its MFs.
Forward pass will then fuzzify the input (value for each MF).
'''
def __init__(self, mfdefs):
super(FuzzifyVariable, self).__init__()
if isinstance(mfdefs, list): # No MF names supplied
mfnames = ['mf{}'.format(i) for i in range(len(mfdefs))]
mfdefs = OrderedDict(zip(mfnames, mfdefs))
self.mfdefs = torch.nn.ModuleDict(mfdefs)
self.padding = 0
@property
def num_mfs(self):
'''Return the actual number of MFs (ignoring any padding)'''
return len(self.mfdefs)
def members(self):
'''
Return an iterator over this variables's membership functions.
Yields tuples of the form (mf-name, MembFunc-object)
'''
return self.mfdefs.items()
def pad_to(self, new_size):
'''
Will pad result of forward-pass (with zeros) so it has new_size,
i.e. as if it had new_size MFs.
'''
self.padding = new_size - len(self.mfdefs)
def fuzzify(self, x):
'''
Yield a list of (mf-name, fuzzy values) for these input values.
'''
for mfname, mfdef in self.mfdefs.items():
yvals = mfdef(x)
yield(mfname, yvals)
def forward(self, x):
'''
Return a tensor giving the membership value for each MF.
x.shape: n_cases
y.shape: n_cases * n_mfs
'''
y_pred = torch.cat([mf(x) for mf in self.mfdefs.values()], dim=1)
if self.padding > 0:
y_pred = torch.cat([y_pred,
torch.zeros(x.shape[0], self.padding)], dim=1)
return y_pred
class FuzzifyLayer(torch.nn.Module):
'''
A list of fuzzy variables, representing the inputs to the FIS.
Forward pass will fuzzify each variable individually.
We pad the variables so they all seem to have the same number of MFs,
as this allows us to put all results in the same tensor.
'''
def __init__(self, varmfs, varnames=None):
super(FuzzifyLayer, self).__init__()
if not varnames:
self.varnames = ['x{}'.format(i) for i in range(len(varmfs))]
else:
self.varnames = list(varnames)
maxmfs = max([var.num_mfs for var in varmfs])
for var in varmfs:
var.pad_to(maxmfs)
self.varmfs = torch.nn.ModuleDict(zip(self.varnames, varmfs))
@property
def num_in(self):
'''Return the number of input variables'''
return len(self.varmfs)
@property
def max_mfs(self):
''' Return the max number of MFs in any variable'''
return max([var.num_mfs for var in self.varmfs.values()])
def __repr__(self):
'''
Print the variables, MFS and their parameters (for info only)
'''
r = ['Input variables']
for varname, members in self.varmfs.items():
r.append('Variable {}'.format(varname))
for mfname, mfdef in members.mfdefs.items():
r.append('- {}: {}({})'.format(mfname,
mfdef.__class__.__name__,
', '.join(['{}={}'.format(n, p.item())
for n, p in mfdef.named_parameters()])))
return '\n'.join(r)
def forward(self, x):
''' Fuzzyify each variable's value using each of its corresponding mfs.
x.shape = n_cases * n_in
y.shape = n_cases * n_in * n_mfs
'''
assert x.shape[1] == self.num_in,\
'{} is wrong no. of input values'.format(self.num_in)
y_pred = torch.stack([var(x[:, i:i+1])
for i, var in enumerate(self.varmfs.values())],
dim=1)
return y_pred
class AntecedentLayer(torch.nn.Module):
'''
Form the 'rules' by taking all possible combinations of the MFs
for each variable. Forward pass then calculates the fire-strengths.
'''
def __init__(self, varlist):
super(AntecedentLayer, self).__init__()
# Count the (actual) mfs for each variable:
mf_count = [var.num_mfs for var in varlist]
# Now make the MF indices for each rule:
mf_indices = itertools.product(*[range(n) for n in mf_count])
self.mf_indices = torch.tensor(list(mf_indices))
# mf_indices.shape is n_rules * n_in
def num_rules(self):
return len(self.mf_indices)
def extra_repr(self, varlist=None):
if not varlist:
return None
row_ants = []
mf_count = [len(fv.mfdefs) for fv in varlist.values()]
for rule_idx in itertools.product(*[range(n) for n in mf_count]):
thisrule = []
for (varname, fv), i in zip(varlist.items(), rule_idx):
thisrule.append('{} is {}'
.format(varname, list(fv.mfdefs.keys())[i]))
row_ants.append(' and '.join(thisrule))
return '\n'.join(row_ants)
def forward(self, x):
''' Calculate the fire-strength for (the antecedent of) each rule
x.shape = n_cases * n_in * n_mfs
y.shape = n_cases * n_rules
'''
# Expand (repeat) the rule indices to equal the batch size:
batch_indices = self.mf_indices.expand((x.shape[0], -1, -1))
# Then use these indices to populate the rule-antecedents
ants = torch.gather(x.transpose(1, 2), 1, batch_indices)
# ants.shape is n_cases * n_rules * n_in
# Last, take the AND (= product) for each rule-antecedent
rules = torch.prod(ants, dim=2)
return rules
class ConsequentLayer(torch.nn.Module):
'''
A simple linear layer to represent the TSK consequents.
Hybrid learning, so use MSE (not BP) to adjust coefficients.
Hence, coeffs are no longer parameters for backprop.
'''
def __init__(self, d_in, d_rule, d_out):
super(ConsequentLayer, self).__init__()
c_shape = torch.Size([d_rule, d_out, d_in+1])
self._coeff = torch.zeros(c_shape, dtype=dtype, requires_grad=True)
@property
def coeff(self):
'''
Record the (current) coefficients for all the rules
coeff.shape: n_rules * n_out * (n_in+1)
'''
return self._coeff
@coeff.setter
def coeff(self, new_coeff):
'''
Record new coefficients for all the rules
coeff: for each rule, for each output variable:
a coefficient for each input variable, plus a constant
'''
assert new_coeff.shape == self.coeff.shape, \
'Coeff shape should be {}, but is actually {}'\
.format(self.coeff.shape, new_coeff.shape)
self._coeff = new_coeff
def fit_coeff(self, x, weights, y_actual):
'''
Use LSE to solve for coeff: y_actual = coeff * (weighted)x
x.shape: n_cases * n_in
weights.shape: n_cases * n_rules
[ coeff.shape: n_rules * n_out * (n_in+1) ]
y.shape: n_cases * n_out
'''
# Append 1 to each list of input vals, for the constant term:
x_plus = torch.cat([x, torch.ones(x.shape[0], 1)], dim=1)
# Shape of weighted_x is n_cases * n_rules * (n_in+1)
weighted_x = torch.einsum('bp, bq -> bpq', weights, x_plus)
# Can't have value 0 for weights, or LSE won't work:
weighted_x[weighted_x == 0] = 1e-12
# Squash x and y down to 2D matrices for gels:
weighted_x_2d = weighted_x.view(weighted_x.shape[0], -1)
y_actual_2d = y_actual.view(y_actual.shape[0], -1)
# Use gels to do LSE, then pick out the solution rows:
try:
coeff_2d, _ = torch.gels(y_actual_2d, weighted_x_2d)
except RuntimeError as e:
print('Internal error in gels', e)
print('Weights are:', weighted_x)
raise e
coeff_2d = coeff_2d[0:weighted_x_2d.shape[1]]
# Reshape to 3D tensor: divide by rules, n_in+1, then swap last 2 dims
self.coeff = coeff_2d.view(weights.shape[1], x.shape[1]+1, -1)\
.transpose(1, 2)
# coeff dim is thus: n_rules * n_out * (n_in+1)
def forward(self, x):
'''
Calculate: y = coeff * x + const [NB: no weights yet]
x.shape: n_cases * n_in
coeff.shape: n_rules * n_out * (n_in+1)
y.shape: n_cases * n_out * n_rules
'''
# Append 1 to each list of input vals, for the constant term:
x_plus = torch.cat([x, torch.ones(x.shape[0], 1)], dim=1)
# Need to switch dimansion for the multipy, then switch back:
y_pred = torch.matmul(self.coeff, x_plus.t())
return y_pred.transpose(0, 2) # swaps cases and rules
class PlainConsequentLayer(ConsequentLayer):
'''
A linear layer to represent the TSK consequents.
Not hybrid learning, so coefficients are backprop-learnable parameters.
'''
def __init__(self, *params):
super(PlainConsequentLayer, self).__init__(*params)
self.register_parameter('coefficients',
torch.nn.Parameter(self._coeff))
@property
def coeff(self):
'''
Record the (current) coefficients for all the rules
coeff.shape: n_rules * n_out * (n_in+1)
'''
return self.coefficients
def fit_coeff(self, x, weights, y_actual):
'''
'''
assert False,\
'Not hybrid learning: I\'m using BP to learn coefficients'
class WeightedSumLayer(torch.nn.Module):
'''
Sum the TSK for each outvar over rules, weighted by fire strengths.
This could/should be layer 5 of the Anfis net.
I don't actually use this class, since it's just one line of code.
'''
def __init__(self):
super(WeightedSumLayer, self).__init__()
def forward(self, weights, tsk):
'''
weights.shape: n_cases * n_rules
tsk.shape: n_cases * n_out * n_rules
y_pred.shape: n_cases * n_out
'''
# Add a dimension to weights to get the bmm to work:
y_pred = torch.bmm(tsk, weights.unsqueeze(2))
return y_pred.squeeze(2)
class AnfisNet(torch.nn.Module):
'''
This is a container for the 5 layers of the ANFIS net.
The forward pass maps inputs to outputs based on current settings,
and then fit_coeff will adjust the TSK coeff using LSE.
'''
def __init__(self, description, invardefs, outvarnames, hybrid=True):
super(AnfisNet, self).__init__()
self.description = description
self.outvarnames = outvarnames
self.hybrid = hybrid
varnames = [v for v, _ in invardefs]
mfdefs = [FuzzifyVariable(mfs) for _, mfs in invardefs]
self.num_in = len(invardefs)
self.num_rules = np.prod([len(mfs) for _, mfs in invardefs])
if self.hybrid:
cl = ConsequentLayer(self.num_in, self.num_rules, self.num_out)
else:
cl = PlainConsequentLayer(self.num_in, self.num_rules, self.num_out)
self.layer = torch.nn.ModuleDict(OrderedDict([
('fuzzify', FuzzifyLayer(mfdefs, varnames)),
('rules', AntecedentLayer(mfdefs)),
# normalisation layer is just implemented as a function.
('consequent', cl),
# weighted-sum layer is just implemented as a function.
]))
@property
def num_out(self):
return len(self.outvarnames)
@property
def coeff(self):
return self.layer['consequent'].coeff
@coeff.setter
def coeff(self, new_coeff):
self.layer['consequent'].coeff = new_coeff
def fit_coeff(self, x, y_actual):
'''
Do a forward pass (to get weights), then fit to y_actual.
Does nothing for a non-hybrid ANFIS, so we have same interface.
'''
if self.hybrid:
self(x)
self.layer['consequent'].fit_coeff(x, self.weights, y_actual)
def input_variables(self):
'''
Return an iterator over this system's input variables.
Yields tuples of the form (var-name, FuzzifyVariable-object)
'''
return self.layer['fuzzify'].varmfs.items()
def output_variables(self):
'''
Return an list of the names of the system's output variables.
'''
return self.outvarnames
def extra_repr(self):
rstr = []
vardefs = self.layer['fuzzify'].varmfs
rule_ants = self.layer['rules'].extra_repr(vardefs).split('\n')
for i, crow in enumerate(self.layer['consequent'].coeff):
rstr.append('Rule {:2d}: IF {}'.format(i, rule_ants[i]))
rstr.append(' '*9+'THEN {}'.format(crow.tolist()))
return '\n'.join(rstr)
def forward(self, x):
'''
Forward pass: run x thru the five layers and return the y values.
I save the outputs from each layer to an instance variable,
as this might be useful for comprehension/debugging.
'''
self.fuzzified = self.layer['fuzzify'](x)
self.raw_weights = self.layer['rules'](self.fuzzified)
self.weights = F.normalize(self.raw_weights, p=1, dim=1)
self.rule_tsk = self.layer['consequent'](x)
# y_pred = self.layer['weighted_sum'](self.weights, self.rule_tsk)
y_pred = torch.bmm(self.rule_tsk, self.weights.unsqueeze(2))
self.y_pred = y_pred.squeeze(2)
return self.y_pred
# These hooks are handy for debugging:
def module_hook(label):
''' Use this module hook like this:
m = AnfisNet()
m.layer.fuzzify.register_backward_hook(module_hook('fuzzify'))
m.layer.consequent.register_backward_hook(modul_hook('consequent'))
'''
return (lambda module, grad_input, grad_output:
print('BP for module', label,
'with out grad:', grad_output,
'and in grad:', grad_input))
def tensor_hook(label):
'''
If you want something more fine-graned, attach this to a tensor.
'''
return (lambda grad:
print('BP for', label, 'with grad:', grad))
================================================
FILE: cluster_data/Aggregation.txt
================================================
15.55 28.65 2
14.9 27.55 2
14.45 28.35 2
14.15 28.8 2
13.75 28.05 2
13.35 28.45 2
13 29.15 2
13.45 27.5 2
13.6 26.5 2
12.8 27.35 2
12.4 27.85 2
12.3 28.4 2
12.2 28.65 2
13.4 25.1 2
12.95 25.95 2
12.9 26.5 2
11.85 27 2
11.35 28 2
11.15 28.7 2
11.25 27.4 2
10.75 27.7 2
10.5 28.35 2
9.65 28.45 2
10.25 27.25 2
10.75 26.55 2
11.7 26.35 2
11.6 25.9 2
11.9 25.05 2
12.6 24.05 2
11.9 24.5 2
11.1 25.2 2
10.55 25.15 2
10.05 25.95 2
9.35 26.6 2
9.3 27.25 2
9.2 27.8 2
7.5 28.25 2
8.55 27.45 2
8.5 27.05 2
8.05 27.2 2
7.85 26.8 2
7.3 27.4 2
6.8 26.85 2
7 26.5 2
7.55 26.3 2
8.55 26.3 2
9 25.85 2
8.6 25.65 2
9.4 25.55 2
8.45 25.05 2
8.85 24.6 2
9.65 24.7 2
10.55 24.35 2
11.05 23.9 2
10.55 23.55 2
9.45 23.35 2
9.2 23.9 2
8.35 23.9 2
7.35 24.75 2
7.4 25.45 2
6.6 25.75 2
6.1 26 2
5.8 26.95 2
5.65 25.8 2
5.3 26.1 2
6.4 25.4 2
5.4 25.25 2
5.35 24.7 2
4.8 25.05 2
4.2 25.55 2
6.4 24.8 2
6.55 24.3 2
7.4 24.25 2
5.45 24.2 2
4.3 24 2
4 24.25 2
3.35 23.3 2
4.85 23.05 2
4.3 22.75 2
5.85 23.4 2
5.9 23.55 2
7.55 23.7 2
6.85 23.25 2
7.65 23.1 2
6.95 22.55 2
6.1 22.6 2
5.5 22.6 2
4.7 22.1 2
3.8 21.85 2
4.65 21.2 2
4.15 20.35 2
5.3 20.4 2
5.6 20.75 2
5.8 21.95 2
6.4 21.95 2
6.55 21.15 2
7.45 21.95 2
7.4 21.55 2
7.75 21.2 2
7.65 20.65 2
6.95 19.8 2
6.6 20.1 2
6.05 20.2 2
5.4 19.65 2
5.35 19.05 2
5.8 18.25 2
6.3 19.1 2
7 18.9 2
7.15 17.9 2
7.35 18.2 2
8.2 20.05 2
8.3 19.45 2
8.3 18.5 2
8.75 18.8 2
9.05 18.2 2
9.35 17.7 2
8.9 17.65 2
8.45 17.2 2
10.05 17.2 2
10.4 16.75 2
8.6 20.9 2
8.65 21.3 2
8.65 21.9 2
8.65 22.5 2
8.95 22.8 2
9.95 22.65 2
8.95 22.2 2
9.65 21.9 2
10.55 22.3 2
10.9 22.85 2
11.35 23.45 2
12.05 23.4 2
12.3 22.75 2
11.7 22.15 2
11.15 22.05 2
10.85 21.5 2
10.85 21.05 2
9.6 21.3 2
9.85 20.7 2
9.35 20.6 2
9.25 19.65 2
9.95 19.8 2
10.7 20.35 2
11.3 20.7 2
12.35 21.6 2
13.1 21.3 2
12.85 20.75 2
12 20 2
11 19.85 2
10.35 19 2
9.9 18.65 2
10.6 18.15 2
11.4 18.3 2
11.4 19.25 2
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================================================
FILE: cluster_data/D31.txt
================================================
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================================================
FILE: cluster_data/R15.txt
================================================
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================================================
FILE: cluster_data/a3.txt
================================================
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gitextract_n4pn8s9r/ ├── .gitignore ├── LICENSE ├── README.md ├── anfis.py ├── cluster_data/ │ ├── Aggregation.txt │ ├── D31.txt │ ├── R15.txt │ ├── a3.txt │ ├── birch3.txt │ └── jain.txt ├── cmeans.py ├── experimental.py ├── fileio/ │ ├── EvaluateXML.java │ ├── astext.py │ ├── fcl.py │ ├── test-model.txt │ ├── test_astext.py │ ├── test_fcl.py │ ├── test_jfml_out.xml │ ├── test_tojfml.py │ └── tojfml.py ├── iris_example.py ├── jang-example4-data.chk ├── jang-example4-data.trn ├── jang_examples.py ├── jang_inverse_example.py ├── jang_pendulum_example.py ├── membership.py ├── sk_examples.py └── vignette_examples.py
SYMBOL INDEX (201 symbols across 15 files)
FILE: anfis.py
class FuzzifyVariable (line 23) | class FuzzifyVariable(torch.nn.Module):
method __init__ (line 28) | def __init__(self, mfdefs):
method num_mfs (line 37) | def num_mfs(self):
method members (line 41) | def members(self):
method pad_to (line 48) | def pad_to(self, new_size):
method fuzzify (line 55) | def fuzzify(self, x):
method forward (line 63) | def forward(self, x):
class FuzzifyLayer (line 76) | class FuzzifyLayer(torch.nn.Module):
method __init__ (line 83) | def __init__(self, varmfs, varnames=None):
method num_in (line 95) | def num_in(self):
method max_mfs (line 100) | def max_mfs(self):
method __repr__ (line 104) | def __repr__(self):
method forward (line 118) | def forward(self, x):
class AntecedentLayer (line 131) | class AntecedentLayer(torch.nn.Module):
method __init__ (line 136) | def __init__(self, varlist):
method num_rules (line 145) | def num_rules(self):
method extra_repr (line 148) | def extra_repr(self, varlist=None):
method forward (line 161) | def forward(self, x):
class ConsequentLayer (line 176) | class ConsequentLayer(torch.nn.Module):
method __init__ (line 182) | def __init__(self, d_in, d_rule, d_out):
method coeff (line 188) | def coeff(self):
method coeff (line 196) | def coeff(self, new_coeff):
method fit_coeff (line 207) | def fit_coeff(self, x, weights, y_actual):
method forward (line 237) | def forward(self, x):
class PlainConsequentLayer (line 251) | class PlainConsequentLayer(ConsequentLayer):
method __init__ (line 256) | def __init__(self, *params):
method coeff (line 262) | def coeff(self):
method fit_coeff (line 269) | def fit_coeff(self, x, weights, y_actual):
class WeightedSumLayer (line 276) | class WeightedSumLayer(torch.nn.Module):
method __init__ (line 282) | def __init__(self):
method forward (line 285) | def forward(self, weights, tsk):
class AnfisNet (line 296) | class AnfisNet(torch.nn.Module):
method __init__ (line 302) | def __init__(self, description, invardefs, outvarnames, hybrid=True):
method num_out (line 324) | def num_out(self):
method coeff (line 328) | def coeff(self):
method coeff (line 332) | def coeff(self, new_coeff):
method fit_coeff (line 335) | def fit_coeff(self, x, y_actual):
method input_variables (line 344) | def input_variables(self):
method output_variables (line 351) | def output_variables(self):
method extra_repr (line 357) | def extra_repr(self):
method forward (line 366) | def forward(self, x):
function module_hook (line 384) | def module_hook(label):
function tensor_hook (line 396) | def tensor_hook(label):
FILE: cmeans.py
class FuzzyCluster (line 27) | class FuzzyCluster(torch.nn.Module):
method __init__ (line 38) | def __init__(self, n_c, n_in, m=1.7):
method set_centroids (line 51) | def set_centroids(self, new_centroids):
method register_centroids (line 54) | def register_centroids(self):
method _cdist (line 65) | def _cdist(x1, x2):
method recalc_centroids (line 80) | def recalc_centroids(self, x, u):
method forward (line 95) | def forward(self, x):
function plot_clusters (line 107) | def plot_clusters(x, fc):
function evaluate_clustering (line 123) | def evaluate_clustering(datset, fc):
function cmeans_cluster (line 134) | def cmeans_cluster(dataset, num_clusters, max_epochs=250, show_plots=True):
function sgd_cluster (line 162) | def sgd_cluster(dataset, num_clusters, epochs=250, show_plots=True):
function read_data (line 199) | def read_data(filename, n_in=2):
function read_and_cluster (line 221) | def read_and_cluster(filename, n_c, n_in=2):
FILE: experimental.py
class TwoLayerNet (line 16) | class TwoLayerNet(torch.nn.Module):
method __init__ (line 21) | def __init__(self, d_in, hidden_size, d_out):
method forward (line 26) | def forward(self, x):
function linear_model (line 32) | def linear_model(x, y, epochs=200, hidden_size=10):
function plotErrors (line 57) | def plotErrors(errors):
function plotResults (line 67) | def plotResults(y_actual, y_predicted):
function _plot_mfs (line 78) | def _plot_mfs(var_name, fv, x):
function plot_all_mfs (line 93) | def plot_all_mfs(model, x):
function calc_error (line 98) | def calc_error(y_pred, y_actual):
function test_anfis (line 107) | def test_anfis(model, data, show_plots=False):
function train_anfis_with (line 123) | def train_anfis_with(model, data, optimizer, criterion,
function train_anfis (line 162) | def train_anfis(model, data, epochs=500, show_plots=False):
FILE: fileio/EvaluateXML.java
class EvaluateXML (line 6) | public class EvaluateXML
method evaluate (line 12) | private static float evaluate(float angle, float change)
method main (line 27) | public static void main(String[] args)
FILE: fileio/astext.py
function _read_comment_line (line 28) | def _read_comment_line(fh, fstr):
function _read_mf_line (line 40) | def _read_mf_line(fh):
function _read_rule_line (line 55) | def _read_rule_line(fh):
function read (line 65) | def read(filename):
function show (line 92) | def show(model, fh=sys.stdout):
function write (line 117) | def write(model, filename):
FILE: fileio/fcl.py
function _in_mf_def (line 24) | def _in_mf_def(mfdef):
function _out_mf_def (line 44) | def _out_mf_def(rule):
function _out_mf_name (line 52) | def _out_mf_name(outnum, rnum):
function _show_antecedents (line 59) | def _show_antecedents(rules, invars):
function show (line 74) | def show(model, fh=sys.stdout):
function write (line 124) | def write(model, filename):
FILE: fileio/test_tojfml.py
function jfml_pendulum_2_domains (line 22) | def jfml_pendulum_2_domains():
function jfml_pendulum_2_model (line 26) | def jfml_pendulum_2_model():
FILE: fileio/tojfml.py
function _in_mf_def (line 28) | def _in_mf_def(mfname, mfdef):
function _out_mf_name (line 51) | def _out_mf_name(outnum, rnum):
function _out_mf_def (line 58) | def _out_mf_def(mfname, rule):
function _mk_antecedents (line 69) | def _mk_antecedents(local_jfml, rules, invars):
function _mk_consequents (line 87) | def _mk_consequents(local_jfml, conseq, onames):
class _LocalJFML (line 104) | class _LocalJFML:
method __init__ (line 110) | def __init__(self):
method set_in_variable (line 114) | def set_in_variable(self, varname, domain):
method set_out_variable (line 122) | def set_out_variable(self, varname):
method get_variable (line 130) | def get_variable(self, varname):
method set_in_term (line 133) | def set_in_term(self, varname, mfname, mfdef):
method set_out_term (line 138) | def set_out_term(self, varname, mfname, coeffs):
method get_term (line 143) | def get_term(self, varname, mfname):
function convert (line 147) | def convert(model, domains):
function write_xml (line 188) | def write_xml(model, domains, filename):
FILE: iris_example.py
function make_one_hot (line 21) | def make_one_hot(data, num_categories, dtype=torch.float):
function get_iris_data_one_hot (line 37) | def get_iris_data_one_hot(in_feat=2, batch_size=1024):
function get_iris_data (line 50) | def get_iris_data(in_feat=2, batch_size=1024):
function vignette_ex5 (line 63) | def vignette_ex5(in_feat=2):
function num_cat_correct (line 80) | def num_cat_correct(model, x, y_actual):
function train_hybrid (line 94) | def train_hybrid(in_feat=2):
function train_non_hybrid (line 112) | def train_non_hybrid(in_feat=2):
FILE: jang_examples.py
function sinc (line 25) | def sinc(x, y):
function make_sinc_xy (line 35) | def make_sinc_xy(batch_size=1024):
function make_sinc_xy_large (line 47) | def make_sinc_xy_large(num_cases=10000, batch_size=1024):
function make_sinc_xy2 (line 59) | def make_sinc_xy2(batch_size=1024):
function ex1_model (line 71) | def ex1_model():
function ex2_eqn (line 86) | def ex2_eqn(x, y, z):
function _make_data_xyz (line 95) | def _make_data_xyz(inp_range):
function ex2_model (line 106) | def ex2_model():
function ex2_training_data (line 117) | def ex2_training_data(batch_size=1024):
function ex2_testing_data (line 126) | def ex2_testing_data():
function ex3_model (line 138) | def ex3_model(mfnum=7):
function ex3_f (line 155) | def ex3_f(u):
function ex3_training_data (line 166) | def ex3_training_data(batch_size=1024):
function ex3_u (line 177) | def ex3_u(k):
function ex3_testing_data (line 193) | def ex3_testing_data():
function ex4_model (line 204) | def ex4_model():
function jang_ex4_trained_model (line 221) | def jang_ex4_trained_model():
function jang_ex4_data (line 268) | def jang_ex4_data(filename):
FILE: jang_inverse_example.py
function random_u (line 26) | def random_u(steps):
function y_next (line 31) | def y_next(k, y, u):
function make_plant_seq (line 42) | def make_plant_seq(steps, u):
function get_training (line 50) | def get_training(size=100, and_plot=True):
function make_training_data (line 65) | def make_training_data(size=100):
function plant_model_untrained (line 76) | def plant_model_untrained():
function u_next (line 94) | def u_next(model, y_now, y_next):
function run_plant_trained (line 103) | def run_plant_trained(steps, model, y_desired, y_init=0.0):
function make_y_desired (line 118) | def make_y_desired(size=100):
function test_control_model (line 127) | def test_control_model(model, size=300, and_plot=True):
FILE: jang_pendulum_example.py
class Pendulum (line 29) | class Pendulum():
method __init__ (line 35) | def __init__(self, theta=0, dtheta=0):
method theta (line 48) | def theta(self):
method dtheta (line 52) | def dtheta(self):
method state (line 56) | def state(self):
method state (line 60) | def state(self, new_state):
method _theta_dot_dot_radians (line 63) | def _theta_dot_dot_radians(self, rtheta, rdtheta, force):
method theta_dot_dot (line 76) | def theta_dot_dot(self, force):
method take_step (line 85) | def take_step(self, force, h=10e-3):
function initial_anfis (line 97) | def initial_anfis():
function jang_traned_anfis (line 113) | def jang_traned_anfis():
class PendulumSystem (line 134) | class PendulumSystem(torch.nn.Module):
method __init__ (line 140) | def __init__(self, theta=0, dtheta=0):
method forward (line 146) | def forward(self, x):
function loss_from (line 168) | def loss_from(trajectory, desired_trajectory, lam=10):
function loss_from_upright (line 183) | def loss_from_upright(trajectory, lam=10):
function plot_errors (line 194) | def plot_errors(errors):
function plot_thetas (line 204) | def plot_thetas(x_data, y_pred):
function train_pendulum (line 222) | def train_pendulum(model, x_data, optimizer,
FILE: membership.py
function _mk_param (line 13) | def _mk_param(val):
class GaussMembFunc (line 20) | class GaussMembFunc(torch.nn.Module):
method __init__ (line 26) | def __init__(self, mu, sigma):
method forward (line 31) | def forward(self, x):
method pretty (line 35) | def pretty(self):
function make_gauss_mfs (line 39) | def make_gauss_mfs(sigma, mu_list):
class BellMembFunc (line 44) | class BellMembFunc(torch.nn.Module):
method __init__ (line 51) | def __init__(self, a, b, c):
method b_log_hook (line 59) | def b_log_hook(grad):
method forward (line 67) | def forward(self, x):
method pretty (line 71) | def pretty(self):
function make_bell_mfs (line 75) | def make_bell_mfs(a, b, clist):
class TriangularMembFunc (line 80) | class TriangularMembFunc(torch.nn.Module):
method __init__ (line 87) | def __init__(self, a, b, c):
method isosceles (line 96) | def isosceles(width, center):
method forward (line 102) | def forward(self, x):
method pretty (line 112) | def pretty(self):
function make_tri_mfs (line 116) | def make_tri_mfs(width, clist):
class TrapezoidalMembFunc (line 121) | class TrapezoidalMembFunc(torch.nn.Module):
method __init__ (line 131) | def __init__(self, a, b, c, d):
method symmetric (line 141) | def symmetric(topwidth, slope, midpt):
method rectangle (line 153) | def rectangle(left, right):
method triangle (line 160) | def triangle(left, midpt, right):
method forward (line 167) | def forward(self, x):
method pretty (line 180) | def pretty(self):
function make_trap_mfs (line 185) | def make_trap_mfs(width, slope, clist):
function make_anfis (line 199) | def make_anfis(x, num_mfs=5, num_out=1, hybrid=True):
FILE: sk_examples.py
class FittingCallback (line 26) | class FittingCallback(Callback):
method __init__ (line 32) | def __init__(self):
method on_epoch_end (line 35) | def on_epoch_end(self, net, dataset_train=None,
class MySimpleNet (line 51) | class MySimpleNet(nn.Module):
method __init__ (line 56) | def __init__(self, num_in, num_feat, num_hidden=10, nonlin=F.relu):
method forward (line 64) | def forward(self, X, **kwargs):
function train_simple_nn (line 72) | def train_simple_nn(X, y, num_in, num_feat):
function fuzzy_classifier (line 84) | def fuzzy_classifier(num_in, num_mfs=5):
function train_fuzzy (line 97) | def train_fuzzy(model, X, y, show_plots=True):
function classify_example (line 116) | def classify_example():
function test_jang (line 131) | def test_jang(show_plots=True):
function test_vignette (line 158) | def test_vignette(show_plots=True):
FILE: vignette_examples.py
function vignette_ex1 (line 22) | def vignette_ex1():
function vignette_ex2 (line 36) | def vignette_ex2():
function vignette_ex3 (line 50) | def vignette_ex3():
function vignette_ex3a (line 64) | def vignette_ex3a():
function vignette_ex3b (line 77) | def vignette_ex3b():
function vignette_ex5 (line 90) | def vignette_ex5():
function vignette_ex1_trained (line 105) | def vignette_ex1_trained():
function vignette_ex5_trained (line 148) | def vignette_ex5_trained():
Condensed preview — 30 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (2,667K chars).
[
{
"path": ".gitignore",
"chars": 1203,
"preview": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packagi"
},
{
"path": "LICENSE",
"chars": 1071,
"preview": "MIT License\n\nCopyright (c) 2019 James F. Power\n\nPermission is hereby granted, free of charge, to any person obtaining a "
},
{
"path": "README.md",
"chars": 2994,
"preview": "# ANFIS in pyTorch #\n\n\nThis is an implementation of the ANFIS system using pyTorch.\n\n\n### ANFIS\n\nANFIS is a way of prese"
},
{
"path": "anfis.py",
"chars": 14937,
"preview": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n'''\n ANFIS in torch: the ANFIS layers\n @author: James Power <james."
},
{
"path": "cluster_data/Aggregation.txt",
"chars": 10427,
"preview": "15.55\t28.65\t2\r\n14.9\t27.55\t2\r\n14.45\t28.35\t2\r\n14.15\t28.8\t2\r\n13.75\t28.05\t2\r\n13.35\t28.45\t2\r\n13\t29.15\t2\r\n13.45\t27.5\t2\r\n13.6\t2"
},
{
"path": "cluster_data/D31.txt",
"chars": 59098,
"preview": "25.0514\t5.7475\t1\r\n26.6614\t7.3414\t1\r\n25.2653\t6.2466\t1\r\n25.2285\t4.7447\t1\r\n25.7529\t5.1564\t1\r\n24.0785\t5.6693\t1\r\n25.2722\t6.86"
},
{
"path": "cluster_data/R15.txt",
"chars": 9548,
"preview": "9.802\t10.132\t1\r\n10.35\t9.768\t1\r\n10.098\t9.988\t1\r\n9.73\t9.91\t1\r\n9.754\t10.43\t1\r\n9.836\t9.902\t1\r\n10.238\t9.866\t1\r\n9.53\t9.862\t1\r\n"
},
{
"path": "cluster_data/a3.txt",
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"path": "cluster_data/birch3.txt",
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"preview": "0.85\t17.45\t2\r\n0.75\t15.6\t2\r\n3.3\t15.45\t2\r\n5.25\t14.2\t2\r\n4.9\t15.65\t2\r\n5.35\t15.85\t2\r\n5.1\t17.9\t2\r\n4.6\t18.25\t2\r\n4.05\t18.75\t2\r\n3"
},
{
"path": "cmeans.py",
"chars": 9168,
"preview": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n'''\n Fuzzy c-means (FCM) clustering in torch: standard and SGD version"
},
{
"path": "experimental.py",
"chars": 6032,
"preview": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\n ANFIS in torch: some simple functions to supply data and plot res"
},
{
"path": "fileio/EvaluateXML.java",
"chars": 1299,
"preview": "import java.io.File;\nimport jfml.JFML;\nimport jfml.FuzzyInferenceSystem;\nimport jfml.knowledgebase.variable.KnowledgeBas"
},
{
"path": "fileio/astext.py",
"chars": 4247,
"preview": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n'''\n ANFIS in torch: read/write an Anfis system as a text file.\n Th"
},
{
"path": "fileio/fcl.py",
"chars": 4768,
"preview": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n'''\n ANFIS in torch: read/write an Anfis system as a FCL file.\n I'm"
},
{
"path": "fileio/test-model.txt",
"chars": 2440,
"preview": "# ANFIS Jang's example 4 (trained)\n# INPUTS xm18 xm12 xm6 x\n# MEMBERS 2 2 2 2\nBellMembFunc 0.17900000512599945 2.0455999"
},
{
"path": "fileio/test_astext.py",
"chars": 518,
"preview": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu May 30 13:50:44 2019\n\n@author: jpower\n\"\"\"\nimport jang_"
},
{
"path": "fileio/test_fcl.py",
"chars": 455,
"preview": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu May 30 13:50:44 2019\n\n@author: jpower\n\"\"\"\nimport jang_"
},
{
"path": "fileio/test_jfml_out.xml",
"chars": 24968,
"preview": "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"yes\"?>\n<fuzzySystem xmlns=\"http://www.ieee1855.org\" name=\"JFML inverted"
},
{
"path": "fileio/test_tojfml.py",
"chars": 4828,
"preview": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n'''\n ANFIS in torch: read/write an Anfis system as a JFML object.\n "
},
{
"path": "fileio/tojfml.py",
"chars": 7500,
"preview": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n'''\n ANFIS in torch: read/write an Anfis system as a JFML object.\n "
},
{
"path": "iris_example.py",
"chars": 5084,
"preview": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n'''\n ANFIS in torch: test cases form the Vignette paper\n This i"
},
{
"path": "jang-example4-data.chk",
"chars": 40500,
"preview": " 9.4542600e-01 1.1003310e+00 1.1291450e+00 1.1431280e+00 1.0043130e+00\n 9.4983000e-01 1.1256890e+00 1.12"
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"path": "jang-example4-data.trn",
"chars": 40500,
"preview": " 9.4786900e-01 1.0659210e+00 1.1351670e+00 1.1392560e+00 1.0516550e+00\n 9.4465700e-01 1.1006470e+00 1.12"
},
{
"path": "jang_examples.py",
"chars": 10988,
"preview": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n'''\n ANFIS in torch: Examples from Jang's paper\n @author: James Pow"
},
{
"path": "jang_inverse_example.py",
"chars": 5401,
"preview": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n'''\n ANFIS in torch: Control examples from Jang's book, chapter 17\n "
},
{
"path": "jang_pendulum_example.py",
"chars": 9700,
"preview": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n'''\n ANFIS in torch: Control examples from Jang's book, chapter 17.\n "
},
{
"path": "membership.py",
"chars": 7557,
"preview": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\n ANFIS in torch: some fuzzy membership functions.\n @author: Jam"
},
{
"path": "sk_examples.py",
"chars": 5134,
"preview": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n'''\n ANFIS in torch: examples showing how to use ANFIS with sklearn vi"
},
{
"path": "vignette_examples.py",
"chars": 10295,
"preview": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n'''\n ANFIS in torch: test cases form the Vignette paper:\n \"ANFI"
}
]
About this extraction
This page contains the full source code of the jfpower/anfis-pytorch GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 30 files (2.4 MB), approximately 634.5k tokens, and a symbol index with 201 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.