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import numpy as np | |
from scipy.optimize import minimize | |
import pylab as plt | |
import itertools | |
num_sides1 = 6 | |
num_sides2 = 10 | |
mean1 = 4.5 | |
mean2 = mean1 |
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cons01 = ({'type': 'eq', 'fun': lambda p: np.sum(p) - 1.}) | |
cons02 = ({'type': 'eq', 'fun': lambda p: np.sum(p) - 1.}) | |
bnds1 = tuple([(0, None)]*(num_sides1)) | |
bnds2 = tuple([(0, None)]*(num_sides2)) |
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sides1 = np.arange(0, num_sides1)+1. | |
sides2 = np.arange(0, num_sides2)+1. | |
cons21 = ({'type': 'eq', 'fun': lambda p: sides1.dot(p) - mean1}) | |
cons22 = ({'type': 'eq', 'fun': lambda q: sides2.dot(q) - mean2}) |
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ini_guess1 = np.array([1/num_sides1]*num_sides1) | |
ini_guess2 = np.array([1/num_sides2]*num_sides2) | |
sol1 = minimize(sum_form, ini_guess1, | |
bounds=bnds1, constraints=[cons01, cons21], options={'maxiter':1001}) | |
sol2 = minimize(sum_form, ini_guess2, | |
bounds=bnds2, constraints=[cons02, cons22], options={'maxiter':1001}) |
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bnds = tuple([(0, None)]*(num_sides2*num_sides1)) | |
# remember number of probabilities | |
lookup_map = {} | |
for ni, piqj in enumerate(itertools.product(sides1, sides2)): | |
lookup_map[ni] = piqj | |
# common guesses | |
guess_common = np.ones(len(lookup_map))/len(lookup_map) |
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sep_sol = [i*j for i,j in itertools.product(sol1.x, sol2.x)] | |
mse = np.sqrt(np.mean((sol_common.x-sep_sol)**2)) | |
print("Difference:", mse) |
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from transformers import AutoTokenizer, AutoModel, TFAutoModel | |
MODEL = "cardiffnlp/twitter-roberta-base" | |
TOKENIZER_EMB = AutoTokenizer.from_pretrained(MODEL) | |
MODEL_EMB = AutoModel.from_pretrained(MODEL) | |
def preprocess(text): | |
new_text = [] | |
for t in text.split(" "): | |
t = '@user' if t.startswith('@') and len(t) > 1 else t |
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import os | |
import json | |
import numpy as np | |
from joblib import load | |
import argparse | |
import pandas as pd | |
from sklearn.ensemble import RandomForestClassifier | |
from sklearn.model_selection import train_test_split | |
from sklearn.metrics import f1_score | |
from joblib import dump, load |
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