109 lines
2.1 KiB
Python
109 lines
2.1 KiB
Python
#%% Change working directory from the workspace root to the ipynb file location. Turn this addition off with the DataScience.changeDirOnImportExport setting
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# ms-python.python added
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import os
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try:
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os.chdir(os.path.join(os.getcwd(), 'docs\\images'))
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print(os.getcwd())
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except:
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pass
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#%%
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import music21
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from music21.midi import MidiFile
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import numpy as np
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import matplotlib.pyplot as plt
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import mido
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#%% [markdown]
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# # midi messages
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#%%
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filepath = '/home/altarin/praca-magisterska/docs/images/seq2seq_generated_midi_7.mid'
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mid = mido.MidiFile(filepath)
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for i, track in enumerate(mid.tracks):
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for msg in track:
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print(msg)
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#%% [markdown]
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# # Regresja liniowa
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#%%
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x = np.arange(0,10)
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y = np.arange(0,10) + np.random.random(10)-0.5
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y_hat = np.arange(0,10)
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plt.scatter(x, y, c='k')
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plt.plot(x,y_hat, c='r')
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# plt.labels
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# plt.savefig('linear_reg.png')
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#%%
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from math import exp
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x = np.arange(-10,10, 0.1)
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# y = np.arange(0,10)
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tanh = lambda x: (exp(x) - exp(-x))/(exp(x) + exp(-x))
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y_hat = [tanh(yy) for yy in x]
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# plt.scatter(x, y, c='k')
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plt.plot(x, y_hat)
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# plt.show()
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# plt.labels
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plt.savefig('tanh.png')
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#%%
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# Gradient descent
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fx = x^2
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dx = 2x + c
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#%%
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func = lambda x: x**2
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func_dx = lambda x:2*x
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x = np.arange(-20,20,0.1)
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y = func(x)
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point_x = -6
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point_y = func(point_x)
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dx = styczna(x)
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learning_points_xs = np.arange(point_x, 0, 0.8)
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learning_points_ys = func(learning_points_xs)
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fig, ax = plt.subplots()
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ax.plot(x, y, c='k')
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for px in learning_points_xs[0:1]:
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slope = func_dx(px)
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intercept = -px**2
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styczna = lambda x: slope*x + intercept
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dx = styczna(x)
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ax.plot(x, dx, c='r', zorder=1)
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ax.scatter(x=point_x, y=point_y, c='r', zorder=6) #start
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# ax.scatter(x=0, y=0, c='g', zorder=6) #min
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# ax.scatter(x=learning_points_xs, y=learning_points_ys, c='y', zorder=5)
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plt.ylim((-20,80))
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plt.xlim((-20,20))
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# plt.xlabel('x')
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# plt.ylabel('f(x) = x^2')
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plt.savefig('gradient_descent_1.png')
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# https://towardsdatascience.com/understanding-the-mathematics-behind-gradient-descent-dde5dc9be06e?
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# https://medium.com/code-heroku/gradient-descent-for-machine-learning-3d871fa48b4c
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#%%
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