Flood_map/model.py at main · Ankit1017/Flood_map · GitHub

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# -*- coding: utf-8 -*-
"""Copy of upper_confidence_bound.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1swscvJa1y8ioQ3Qp-8zW1gCWcqc7QYR-

# Upper Confidence Bound (UCB)

## Importing the libraries
"""

import numpy as np

import pandas as pd

"""## Importing the dataset"""

dataset = pd.read_csv('Book1.csv')

"""## Implementing UCB"""

import math

def mod(pt):
    N = pt#dropbox ka selection idhar aayega but by default yeh value honi chahiye
    d = 10#column daal diyo
    ads_selected = []
    numbers_of_selections = [0] * d
    sums_of_rewards = [0] * d
    total_reward = 0
    for n in range(0, N):
        ad = 0
        max_upper_bound = 0
        for i in range(0, d):
            if (numbers_of_selections[i] > 0):
                average_reward = sums_of_rewards[i] / numbers_of_selections[i]
                delta_i = math.sqrt(3/2 * math.log(n + 1) / numbers_of_selections[i])
                upper_bound = average_reward + delta_i
            else:
                upper_bound = 1e400
            if upper_bound > max_upper_bound:
                max_upper_bound = upper_bound
                ad = i
        ads_selected.append(ad)
        numbers_of_selections[ad] = numbers_of_selections[ad] + 1
        reward = dataset.values[n, ad]
        sums_of_rewards[ad] = sums_of_rewards[ad] + reward
        total_reward = total_reward + reward

    """## Visualising the results"""
    p=ads_selected[len(ads_selected)-1]
    print(numbers_of_selections[p]/N)
    return (ads_selected[len(ads_selected)-1])