Wael Ben Amara waelbenamara

## Dataset.csv

          
            priceUsd
            moving_average_1
            moving_average_2
            crossover
            linear_regression
            label

            
              4261.48
              
              
              0
              1
              1

            
              4261.48
              
              
              0
              1
              1

            
              4280.56
              
              
              0
              1
              0

            
              4261.48
              
              
              0
              1
              1

            
              4261.48
              4265.2959999999985
              
              0
              1
              1

            
              4261.48
              4265.2959999999985
              
              0
              0
              1

            
              4261.48
              4265.2959999999985
              
              0
              0
              1

            
              4261.48
              4261.48
              
              0
              0
              1

            
              4261.48
              4261.48
              
              0
              0
              1

## alice_bob_gibbs_sampler.py
"""

This code demonstrates Gibbs sampling over a probabilistic database
with two delta-tuples representing dietary preferences constrained by
two query-answer observations.
ref = https://openproceedings.org/2022/conf/edbt/paper-66.pdf
Gamma Probabilistic Databases:
Learning from Exchangeable Query-Answers
"""

## dirichlet.py
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from mpl_toolkits.mplot3d.art3d import Poly3DCollection

alpha = np.array([2, 2, 2, 2])

def barycentric_to_cartesian(bary):
    vertices = np.array([
        [1, 0, 0],

## alice_diertary_example.py
"""
Dirichlet-Categorical Bayesian Learning: Alice's Dietary Preference
Exact Implementation of Thesis Example with Gibbs Sampling
4-Category Model: Vegan, Vegetarian, Pescatarian, Omnivore

Run this script to generate all CSVs and plots.
Results will be saved in the current directory.
"""

import numpy as np
priceUsd	moving_average_1	moving_average_2	crossover	linear_regression	label
4261.48			0	1	1
4261.48			0	1	1
4280.56			0	1	0
4261.48			0	1	1
4261.48	4265.2959999999985		0	1	1
4261.48	4265.2959999999985		0	0	1
4261.48	4265.2959999999985		0	0	1
4261.48	4261.48		0	0	1
4261.48	4261.48		0	0	1
	"""

	This code demonstrates Gibbs sampling over a probabilistic database
	with two delta-tuples representing dietary preferences constrained by
	two query-answer observations.
	ref = https://openproceedings.org/2022/conf/edbt/paper-66.pdf
	Gamma Probabilistic Databases:
	Learning from Exchangeable Query-Answers
	"""
	import numpy as np
	import matplotlib.pyplot as plt
	from mpl_toolkits.mplot3d import Axes3D
	from mpl_toolkits.mplot3d.art3d import Poly3DCollection

	alpha = np.array([2, 2, 2, 2])

	def barycentric_to_cartesian(bary):
	vertices = np.array([
	[1, 0, 0],
	"""
	Dirichlet-Categorical Bayesian Learning: Alice's Dietary Preference
	Exact Implementation of Thesis Example with Gibbs Sampling
	4-Category Model: Vegan, Vegetarian, Pescatarian, Omnivore

	Run this script to generate all CSVs and plots.
	Results will be saved in the current directory.
	"""

	import numpy as np