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+from library.ext_prowras import ProWRAS_gen
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+from library.interfaces import GanBaseClass
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+
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+
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+class ProWRAS(GanBaseClass):
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+ """
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+ This is a toy example of a GAN.
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+ It repeats the first point of the training-data-set.
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+ """
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+
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+ def __init__(self
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+ , max_levels = 5
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+ , convex_nbd = 5
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+ , n_neighbors = 5
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+ , max_concov = None
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+ , theta = 1.0
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+ , shadow = 100
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+ , sigma = 0.000001
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+ , n_jobs = 1
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+ , debug = False
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+ ):
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+ """
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+ Initializes the class and mark it as untrained.
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+ """
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+ self.data = None
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+ self.max_levels = max_levels
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+ self.convex_nbd = convex_nbd
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+ self.n_neighbors = n_neighbors
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+ self.max_concov = max_concov
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+ self.theta = theta
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+ self.shadow = shadow
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+ self.sigma = sigma
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+ self.n_jobs = n_jobs
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+ self.debug = debug
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+
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+ def reset(self):
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+ """
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+ Resets the trained GAN to an random state.
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+ """
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+ pass
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+
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+ def train(self, dataSet):
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+ """
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+ Trains the GAN.
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+
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+ It stores the first data-point in the training data-set and mark the GAN as trained.
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+
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+ *dataSet* is a instance of /library.dataset.DataSet/. It contains the training dataset.
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+ We are only interested in the class 1.
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+ """
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+ self.data = dataSet
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+
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+ def generateDataPoint(self):
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+ """
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+ Generates one synthetic data-point by copying the stored data point.
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+ """
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+ return self.generateData(1)[0]
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+
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+ def generateData(self, numOfSamples=1):
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+ """
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+ Generates a list of synthetic data-points.
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+
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+ *numOfSamples* is a integer > 0. It gives the number of new generated samples.
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+ """
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+ if self.max_concov is not None:
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+ max_concov = self.max_concov
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+ else:
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+ max_concov = self.data.data.shape[0]
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+
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+ return ProWRAS_gen(
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+ data = self.data.data,
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+ labels = self.data.labels,
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+ max_levels = self.max_levels,
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+ convex_nbd = self.convex_nbd,
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+ n_neighbors = self.n_neighbors,
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+ max_concov = max_concov,
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+ num_samples_to_generate = numOfSamples,
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+ theta = self.theta,
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+ shadow = self.shadow,
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+ sigma = self.sigma,
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+ n_jobs = self.n_jobs,
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+ enableDebug = self.debug)[0][:numOfSamples]
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