def add_model_fuz(self, filename, modelname):
""" called from: fileOpen
adds a new model from filesystem (.fis) with:
in: filename incl. path str
modelname str
out: name of the new model or None
model_new str
if model_name is already in dict self.d_fuz then:
add a 1 to the name to make it unique """
modelname_new = modelname
while(modelname_new in self.d_fuzz):
modelname_new += "1"
self.fx = Pyfuzzy.read_model(filename, DEBUG=1)
#~ for i in range(len(self.fx.inputs)):
#~ for li in self.fx.inputs[i]:
#~ for j in range(len(li)):
#~ print "self.fx.inputs:: ", self.fx.inputs[i][j]
li_inp = self.fx.get_inputs()
for i in range(len(li_inp)):
li_memb = self.get_members_of_input(li_inp[i])
self.d_fuzz[modelname_new] = self.fx
return modelname_new # fx only for inside of class worker
def add_model_fuz(self, filename, modelname):
"""
adds a new model from filesystem (.fis) with:
in: filename incl. path str
modelname str
out: name of the new model or None
dataset_new str
if dataset_name is already in dict self.d_models_fuz then:
add a 1 to the name to make it unique
result: add the model in self.d_models_fuz an in TREE
"""
ds_new = modelname
#while(ds_new in self.d_models_fuz):
while((ds_new in self.d_models_svm) or (ds_new in self.d_models_fuz)):
ds_new += "1"
fx = Pyfuzzy.read_model(filename)
self.d_models_fuz[ds_new] = fx
#print self.d_models_fuz.items()
return ds_new
#!/usr/bin/env python
# reads a one dimensional fuzzy model and a training data set
# to train rules
# the result will be stored as fuzzy model
import sys
sys.path.append('../')
import Pyfuzzy as fuzz
# adapt this for different tests
f1=fuzz.read_model('hab_schreiadler.fis',DEBUG=1)
x,y=f1.read_training_data('hab_data.csv', header=1)
f1.train_rules(x,y,0.75) # check the influence of different alpha
f1.store_model('hab')
#!/usr/bin/env python
# reads in a fuzzy model and generates a noisy set of data for
# training of it
import sys
sys.path.append('../')
import Pyfuzzy as fuzz
import numpy as np
# change this for different models
f1 = fuzz.read_model('nahr_schreiadler.fis', DEBUG=0)
datasize = 500
x1min = 0.0
x1max = 1.0
x2min = 0.0
x2max = 300.0
# generate the training data
print 'x1,x2,y'
for i in range(datasize):
x1 = (x1max - x1min) * np.random.rand() + x1min
x2 = (x2max - x2min) * np.random.rand() + x2min
y = f1.calc2(x1, x2)
y = np.random.normal(y, 0.1) # add some noise
print x1, x2, y
#!/usr/bin/env python
# reads a one dimensional fuzzy model and a training data set
# to train rules
# the result will be stored as fuzzy model
import sys
sys.path.append('../')
import Pyfuzzy as fuzz
# adapt this for different tests
f1=fuzz.read_model('nahr_schreiadler.fis',DEBUG=1)
x,y=f1.read_training_data('nahr_data.csv', header=1)
f1.train_rules(x,y,0.75) # check the influence of different alpha
f1.store_model('nahr')
#!/usr/bin/env python
# reads a one dimensional fuzzy model and a training data set
# to train rules
# the result will be stored as fuzzy model
import sys
sys.path.append('../')
import Pyfuzzy as fuzz
# adapt this for different tests
f1 = fuzz.read_model('hab_schreiadler.fis', DEBUG=1)
x, y = f1.read_training_data('hab_data.csv', header=1)
f1.train_rules(x, y, 0.75) # check the influence of different alpha
f1.store_model('hab')
#!/usr/bin/env python
# reads a one dimensional fuzzy model and a training data set
# to train rules
# the result will be stored as fuzzy model
import sys
sys.path.append('../')
import Pyfuzzy as fuzz
# adapt this for different tests
f1 = fuzz.read_model('gen_data1.fis', DEBUG=1)
x, y = f1.read_training_data('data1.csv', header=1)
f1.train_rules(x, y, 0.75) # check the influence of different alpha
f1.store_model('data1')
Landscape, Wind, Climate
"""
import sys
import os
sys.path.append(os.environ["SAMT2MASTER"] + '/fuzzy/src')
import Pyfuzzy as fuzz
sys.path.append(os.environ["SAMT2MASTER"] + '/src')
import grid as samt2
import time
# load fuzzy model
# path='/home/kerkow/Aufbau_Fuzzy/Modell/Version3/'
# path='/datadisk/pya/culifo_June_2018/culifo_regional/'
path = '/datadisk/Mosquito-Modeling/Regional/'
f = fuzz.read_model(path + 'LandscapeMosquitoes3.fis')
path = '/datadisk/Mosquito-Modeling/Regional/data/'
# load the ASCII or HDF-files
landscape = samt2.grid()
landscape.read_ascii(path + 'Landscape_Version2_MW_7.asc')
wind = samt2.grid()
wind.read_hdf(path + 'wind100m.hdf', 'wind100m')
climate = samt2.grid()
climate.read_ascii(path + 'ClimateSuitability_1981_2010_100m.asc')
#climate.read_ascii(path+'ClimateModelOutputs_callibrated/ClimateSuitability_2051_2080_100m.asc')
#
# start the simulation
t0 = time.time()
modell = f.grid_calc3(landscape, wind, climate)
print('calc time:', time.time() - t0)
#!/usr/bin/env python
# reads a one dimensional fuzzy model and a training data set
# to train rules
# the result will be stored as fuzzy model
import sys
sys.path.append('../')
import Pyfuzzy as fuzz
# adapt this for different tests
f1=fuzz.read_model('gen_data1.fis',DEBUG=1)
x,y=f1.read_training_data('data1.csv', header=1)
f1.train_rules(x,y,0.75) # check the influence of different alpha
f1.store_model('data1')
