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61 lines
2.0 KiB
61 lines
2.0 KiB
2 years ago
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# -*- coding: utf-8 -*-
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import numpy as np
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from tqdm import trange
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import matplotlib.pyplot as plt
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from decorators import multi_input
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@multi_input
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def estimate_dimension(X, max_size, test_size = 30, Nsteps = 20, fraction = 0.5):
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"""
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Plots an estimation of the dimension of a dataset at different scales
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Parameters
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----------
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X: dataframe(n_datapoints, n_features):
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Dataframe containing the data
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max_size : float
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The upper bound for the scale
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test_size : int, optional, default 30
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The number of datapoints used to estimate the density
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Nsteps : int, optional, default 20
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The number of different scales at which the density is estimated
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fraction : float between 0 and 1, optional, default 0.5
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Difference in radius between the large sphere and smaller sphere used to compute density
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Returns
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-------
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average : ndarray(Nsteps)
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The dimension at each scale
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"""
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average = np.zeros(Nsteps)
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S = X.iloc[np.random.choice(X.shape[0], test_size, replace=False)]
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iterator = trange(0, Nsteps, position=0, leave=True)
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iterator.set_description("Estimating dimension")
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for n in iterator:
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size = max_size*n/Nsteps
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count_small = np.zeros(X.shape[0])
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count_large = np.zeros(X.shape[0])
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dimension = np.zeros(S.shape[0])
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for i in range(0,S.shape[0]):
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for j in range(0,X.shape[0]):
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distance = np.sqrt(np.square(S.iloc[i] - X.iloc[j]).sum())
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if (distance < size/fraction):
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count_large[i] += 1
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if (distance < size):
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count_small[i] += 1
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if (count_large[i] != 0):
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dimension[i] = np.log(count_small[i]/count_large[i])/np.log(fraction)
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else:
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dimension[i] = 0
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average[n] = np.mean(dimension)
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plt.plot(range(0, Nsteps), average)
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plt.xlabel("Scale")
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plt.ylabel("Dimension")
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plt.show()
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return average
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