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""" | |
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy) | |
BSD License | |
""" | |
import numpy as np | |
# data I/O | |
data = open('input.txt', 'r').read() # should be simple plain text file | |
chars = list(set(data)) | |
data_size, vocab_size = len(data), len(chars) |
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import numpy as np | |
import pylab as pl | |
from numpy import fft | |
def fourierExtrapolation(x, n_predict): | |
n = x.size | |
n_harm = 10 # number of harmonics in model | |
t = np.arange(0, n) | |
p = np.polyfit(t, x, 1) # find linear trend in x | |
x_notrend = x - p[0] * t # detrended x |
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""" | |
License: BSD | |
Author: Mathieu Blondel | |
Implements three algorithms for projecting a vector onto the simplex: sort, pivot and bisection. | |
For details and references, see the following paper: | |
Large-scale Multiclass Support Vector Machine Training via Euclidean Projection onto the Simplex | |
Mathieu Blondel, Akinori Fujino, and Naonori Ueda. |
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