BenjaminFraser

BenjaminFraser

class RandomForest():
    """ Python implementation of a random forest regressor """
    def __init__(self, x, y, num_trees, sample_size, feature_proportion=1.0, 
                 min_leaf=5, bootstrap=False, random_seed=12):
        np.random.seed(random_seed)
        self.x = x
        self.y = y
        self.num_trees = num_trees
        self.sample_size = sample_size
        self.feature_proportion = feature_proportion

BenjaminFraser

class DecisionTree():
    """ Form a basic decision tree """
    def __init__(self, x, y, idxs=None, oob_idxs=None, 
                 min_leaf=5, feat_proportion=1.0):
        if idxs is None:
            idxs = np.arange(len(y))
        self.x = x
        self.y = y
        self.idxs = idxs
        self.oob_idxs = oob_idxs

BenjaminFraser

from bs4 import BeautifulSoup
import requests
from time import sleep

# select Guardian website - Military news and obtain a set of URLs
crawl_url = 'https://www.theguardian.com/uk/military?page='

# form a set to store unique urls of all articles
guardian_urls = set()

BenjaminFraser

from nltk.sentiment.vader import SentimentIntensityAnalyzer
sent_i = SentimentIntensityAnalyzer()

def vadar_sentiment(text):
    """ Calculate and return the nltk vadar (lexicon method) sentiment """
    return sent_i.polarity_scores(text)['compound']

# create new column for vadar compound sentiment score
news_sentiments['vadar compound'] = news_sentiments['title'].apply(vadar_sentiment)

BenjaminFraser

class Adaline(object):
    """ Adaline (Adaptive Linear Neuron) for binary classification.
        Minimises the cost function using gradient descent. """

    def __init__(self, learn_rate = 0.01, iterations = 100):
        self.learn_rate = learn_rate
        self.iterations = iterations


    def fit(self, X, y, biased_X = False, standardised_X = False):

BenjaminFraser

class Perceptron(object):
    """ Perceptron for demonstrating a binary classifier """

    def __init__(self, learn_rate = 0.01, iterations = 100):
        self.learn_rate = learn_rate
        self.iterations = iterations


    def fit(self, X, y, biased_X = False):
        """ Fit training data to our model """

BenjaminFraser

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

# make up random data for bird wingspans and weights for golden eagles and horned owls
bird_wingspans = np.concatenate([np.random.randint(170, 230, size = 50)/100.0, 
                                 np.random.randint(60, 100, size = 50)/100.0])
bird_weights = np.concatenate([(11 - 10)*np.random.randn(50)+10, np.abs(np.random.randn(50))+1])

# combine X vectors into a 2-dimensional array with 100 rows and 2 columns

BenjaminFraser

# Arda Mavi
import os
import numpy as np
from os import listdir
from scipy.misc import imread, imresize
from keras.utils import to_categorical
from sklearn.model_selection import train_test_split

# Settings:
img_size = 64

BenjaminFraser

/*************************************************************************
 * Remote node - nRF24L01+ radio communications                          *
 *      A program to operate a remote-node slave device that sends       *
 *      data to a command unit on a given request message. The radio     *            
 *      transceiver used is the nRF24L01+, and it operates using the     *
 *      TMRh20 RF24 library.                                             *
 *                                                                       *
 *      Author: B.D. Fraser                                              *
 *                                                                       *
 *        Last modified: 27/06/2017                                      *