4 Matching Annotations
- Nov 2021
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www.tensorflow.org www.tensorflow.org
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If you don't have that information, you can determine which frequencies are important by extracting features with Fast Fourier Transform. To check the assumptions, here is the tf.signal.rfft of the temperature over time. Note the obvious peaks at frequencies near 1/year and 1/day:
Do a fft with tensorflow
fft = tf.signal.rfft(df['T (degC)']) f_per_dataset = np.arange(0, len(fft)) n_samples_h = len(df['T (degC)']) hours_per_year = 24*365.2524 years_per_dataset = n_samples_h/(hours_per_year) f_per_year = f_per_dataset/years_per_dataset plt.step(f_per_year, np.abs(fft)) plt.xscale('log') plt.ylim(0, 400000) plt.xlim([0.1, max(plt.xlim())]) plt.xticks([1, 365.2524], labels=['1/Year', '1/day']) _ = plt.xlabel('Frequency (log scale)')
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outline.com outline.com
- Sep 2021
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scikit-learn.org scikit-learn.org
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max_iterint, default=1000The maximum number of passes over the training data (aka epochs).
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- Nov 2019
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scolaire.loupbrun.ca scolaire.loupbrun.ca
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j’avais l’ambition de progresser à l’infini
Beauvoir évoque le caractère difficilement saisissable (et potentiellement sans fin, infini) du devenir, du moi en changement.
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