GastonMazzei

import plotly.graph_objects as go
import pandas as pd

df = pd.read_csv('path/to/file.csv')

# You might want to change the "wanted columns"
wanted_columns = ['time']
wanted_columns += [f'Question {x+1}' for x in range(6)]
wanted_columns += [f'Answer {x+1}' for x in range(6)]

GastonMazzei

#********************************************************************************
# we are using the conditional_scope of the keras_tuner hyperparameter class
#
# link: https://keras.io/api/keras_tuner/hyperparameters/#hyperparameters-class
#
# example by Gaston Mazzei, https://gastonmazzei.github.io/
#******************************************************************************** 

GastonMazzei

import numpy as np

import matplotlib.pyplot as plt

from sklearn.ensemble import GradientBoostingClassifier

# Definimos la semilla (SEED) y la cantidad de filas (N)
SEED=1234
N = 2500
np.random.seed(SEED)

GastonMazzei

import matplotlib.pyplot as plt
import numpy as np

x=np.linspace(0,15,100)
y=np.sin(x)

plt.scatter(x,y)
plt.plot(x,y)
plt.title('PMF of the $2^n$ different possible phases')
plt.ylabel('Probability (0-1)')

GastonMazzei

import numpy as np

"""
Simple code to show how the minimization of the coefficient of variation (i.e. propto the standard deviation) 
is equivalent to the maximization of the entropy for the probability distribution model of the number of counts over the total, 
i.e. Dirichlet distribution for multinomial variables approximated by the maximum likelihood :-)

Mathematical equivalence to render in Latex/MathJax:
max_x(-\sum_{i=1}^4\frac{N_i(x)}{Ntot(x)}log(\frac{N_i(x)}{Ntot(x)})) = min_x(\sum_{i=1}^4(\frac{N_i(x)-\bar{N}(x)}{\bar{N}(x)})^2) 

GastonMazzei

import matplotlib.pyplot as plt
import numpy as np

from PIL import Image
fig = plt.figure()
im = Image.open('ball.jpg')
L = 15
im = np.asarray(im.resize((im.size[0]//L, im.size[1]//L))).astype(np.float)/ 255
CENTER = fig.bbox.xmax//2, fig.bbox.ymax//4 
DX,DY = -190,280#fig.bbox.xmax//10, fig.bbox.ymax //5

GastonMazzei

import numpy as np
import matplotlib.pyplot as plt

from uuid import uuid4

"""
Distributed Self-stabilizing Algorithms:
  Compute locally the ring's size.
  assuming all variables are initialized randomly.