aliakbars

import pymc3 as pm

da_points_solved = [47,55,39,36,27,37,31,33,39,38,20,52,36,40,10,41,42,11,20,15,26,30,11,22,25,19,51,29,17,29,39,30,54,35]
da_points = [82,68,64,45,27,48,43,46,52,46,39,59,55,59,35,65,61,18,27,37,38,42,34,27,30,37,51,36,30,42,47,47,62,57]

sw_points_solved = [8,11,6,6,12,12,7,15,13,6,17,12,3,11,6,7,6,9,10,0,0,6,17,16,19,9,15,10,14,8,24,17]
sw_points = [11,17,12,12,12,12,16,16,13,16,22,20,3,11,6,17,13,15,10,10,10,6,17,16,24,14,15,15,24,15,24,17]

with pm.Model() as model:
    p_sw = pm.Beta('completion_rate_sw', alpha=1, beta=1)

aliakbars

import pymc3 as pm

with pm.Model() as coinflip:
    which_coin = pm.Bernoulli('which_coin', 0.5)
    theta_biased = pm.Uniform('theta_biased', 0, 1)
    p = pm.math.switch(which_coin > 0.5, 0.5, theta_biased)
    heads = pm.Binomial('heads', n=2, p=p, observed=2)
    
    coin_trace = pm.sample(5000, tune=2500)

aliakbars

from spacy.lang.id import Indonesian

nlp = Indonesian()
# additional "stop words"
# bisa diganti dengan yang lain
nlp.Defaults.stop_words.update(['nya', 'yg', 'aja', 'deh', 'ny', 'dr', 'sy', 'ya','klo','sdh',
                               'udah','sampe','dah','tp','ga','gk','sih','gak','tdk','e','dgn','sm'])

def tokenizer(text):
    # hanya mengambil lemma saja, bisa diganti yang lain jika dibutuhkan

aliakbars

import matplotlib.pyplot as plt
from sklearn.datasets.samples_generator import make_blobs
from sklearn.cluster import KMeans

# Anda dapat mengganti nilai X dan y sesuai dengan kebutuhan Anda
X, y = make_blobs(n_samples=300, centers=4,
                       cluster_std=1.2, random_state=3)

inertia = []
for k in range(1,11):