def test_from_file(samples_filename, test_samples_filename, results_filename):
""" Perform test on network from specified sample filenames """
sample_set = SampleSet()
sample_set.init_from_file(samples_filename)
testing_set = SampleSet()
testing_set.init_from_file(test_samples_filename)
perform_test(sample_set, testing_set, results_filename)
def random_pattern_test(sample_set_size, vector_size):
""" Perform test on network using random patterns """
pattern_gen = PatternGen(vector_size)
samples = []
for _ in range(sample_set_size):
samples.append(pattern_gen.generate_random_pattern())
sample_set = SampleSet(samples)
perform_test(sample_set, sample_set)
def train_network(samples_filename, weights_filename):
""" Perform training on network from file and saved weights. """
sample_set = SampleSet()
sample_set.init_from_file(samples_filename)
net = HopfieldNet(sample_set.sample_size)
net.initialize()
for sample in sample_set:
net.train(sample)
save_weights_to_file(net.weights, weights_filename)
def test_network(weights_filename, samples_filename):
""" Perform testing on network from weights file."""
weights = read_weights_from_file(weights_filename)
net = HopfieldNet(len(weights), weights)
sample_set = SampleSet()
sample_set.init_from_file(samples_filename)
results = []
for sample in sample_set:
results.append(net.test(sample))
return results
def __init__(self, fname='', csvfile='', backgroundnum=10000):
self.fs = FeatureSpace(fname)
self.featurename = list(self.fs.layers.keys())
self.sampleset = SampleSet(csvfile)
self.sampleset.getbgvalues(self.fs)
bglist = self.fs.layers['h_dem'].getRDsamples(backgroundnum)
self.bgset = self.fs.getlayervalues(bglist)
self.density = []
self.linearPredictor = np.zeros(backgroundnum)
self.numFeatures = len(self.fs.layers.keys())
self.features = []
self.featureGenerators = []
self.linearPredictorNormalizer = 0.0
self.densityNormalizer = self.bgset.shape[0]
self.entropy = -1.0
self.reg = 0
self.iteration = -1
self.activeLayer = {'ecoreg', 'h_dem', 'tmp6190_ann'}
# self.hinge = []
# self.revhinge = []
#
# def computecell(self, val):
# pass
#
# def computegrid(self):
# pass
#
if __name__ == '__main__':
fname = r'D:\test\SJY\asc'
csvfile = r'D:\test\SJY\with9factors\settlements_samplePredictions.csv'
lambdafile = r'D:\test\SJY\with9factors\settlements.lambdas'
fs = FeatureSpace(fname)
ss = SampleSet(csvfile)
l = Lambdas(lambdafile)
l.parselambdafile()
ss.getbgvalues(fs)
dt = ss.getfeatures()
for nm in dt.columns:
for v in l.lambdas:
if nm in v.name:
print(nm, v)
res = []
for v in l.lambdas:
if isinstance(v, Product):
g1, g2 = v.name
pp = v.eval(ss.csv[g1], ss.csv[g2]) * v.lam
