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import numpy as np |
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import _pickle |
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import pandas as pd |
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from utils.ndgan import DCGAN |
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np.random.seed(4269) |
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class dataset(): |
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""" Creates dataset from input source |
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""" |
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def __init__(self,number_samples:int, name:str,source:str,boundary_conditions:list=None): |
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""" Init |
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Args: |
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number_samples (int): number of samples to be genarated |
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name (str): name of dataset |
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source (str): source file |
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boundary_conditions (list): y1,y2,x1,x2 |
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""" |
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self.sample_size = number_samples |
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self.name = name |
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self.samples = [] |
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self.encoding_dim = 8 |
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self.latent_dim = 16 |
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self.source = source |
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self.boundary_conditions = boundary_conditions |
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def generate(self): |
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with open(f"./data/{self.source}", "rb") as input_file: |
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local = pd.read_csv(input_file) |
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dfs = local.drop("Name",axis=1) |
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dfs = (dfs-dfs.min())/(dfs.max()-dfs.min()) |
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dfs = pd.concat([local.Name,dfs],1) |
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self.vae = DCGAN(self.latent_dim,dfs) |
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self.vae.start_training() |
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self.samples = self.vae.predict(self.sample_size) |
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if self.boundary_conditions: |
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self.samples=self.samples[((self.samples[:,0]>self.boundary_conditions[2]) & (self.samples[:,0] < self.boundary_conditions[-1]))&((self.samples[:,0]>self.boundary_conditions[0]) & (self.samples[:,0] < self.boundary_conditions[1]))] |
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print("Samples:",self.samples) |
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dataframe = pd.concat([dfs,pd.DataFrame(self.samples,columns=dfs.columns[1:])]) |
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dataframe.to_pickle(f'./data/{self.name}') |
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print(dataframe) |
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return dataframe |
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