Deleting some misc files

1 files changed, 0 insertions, 100 deletions

diff --git a/gettingStarted.py b/gettingStarted.py
deleted file mode 100644
index 3a8d907..0000000
--- a/gettingStarted.py
+++ /dev/null
@@ -1,100 +0,0 @@
-import numpy as np

-import random

-import math

-from copy import deepcopy

-

-credit_data = np.genfromtxt('/Users/mikevink/Documents/python/2020_data_mining_assignments/credit_score.txt', delimiter=',', skip_header=True)

-

-#print(credit_data)

-#print(credit_data[0])

-#print(credit_data[:,3])

-#print(credit_data[4,0])

-#print(np.sort(np.unique(credit_data[:,3]))) #Give the distinct values of income, sorted from low to high

-#print(np.sum(credit_data[:,5]))

-#print(credit_data.sum(axis=0)) #Add the entries of each column of credit_data

-#print(credit_data.sum(axis=1)) #Add the entries of each row

-#print(credit_data[credit_data[:,0] > 27]) # Select all rows where the first column is bigger than 27

-#

-#x = np.array([2, 5, 10])

-#print(x)

-#print(np.arange(0, 10))

-#

-#print(np.arange(0, 10)[credit_data[:,0] > 27]) #Select the *row numbers* of the rows where the first column of credit_data is bigger than 27

-#

-#index = np.random.choice(np.arange(0, 10), size=5, replace=False) #Draw a random sample of size 5 from the numbers 1 through 10 (without replacement)

-#print(index)

-#train = credit_data[index,]

-#print(train)

-#test = np.delete(credit_data, index, axis=0) #Select all rows with row number not in "index"

-#print(test)

-#

-#print(random.choice(train))

-

-

-### Practice exercise 1 ###

-def impurity(vector): # vector = list of 0s and 1s

-    num_of_class_labels = len(vector)

-    num_of_class_1 = sum(vector)

-    num_of_class_0 = num_of_class_labels - num_of_class_1

-    return (num_of_class_0 / num_of_class_labels) * (num_of_class_1 / num_of_class_labels)

-

-array=np.array([1,0,1,1,1,0,0,1,1,0,1])

-print(impurity(array))

-

-

-### Practice exercise 2 ###

-def bestsplit(x, y): # x = numeric values; y = class labels

-    x_sorted = np.sort(np.unique(x))

-    split_points = (x_sorted[:len(x_sorted)-1] + x_sorted[1:]) / 2

-    

-    best_impurity_after_split = math.inf

-    for split in split_points:

-        impurity_after_split = impurity(y[x <= split]) + impurity(y[x > split])

-        if impurity_after_split < best_impurity_after_split:

-            best_split = split

-            best_impurity_after_split = impurity_after_split

-

-    return best_split

-

-print(bestsplit(credit_data[:,3], credit_data[:,5]))

-

-

-

-class Node:

-    def _init_(self):

-        self.left  = None

-        self.right = None

-        self.split_value = None

-        

-class Leaf:

-    def __init__(self, predicted_class: int):

-        self.predicted_class = predicted_class

-

-

-def tree_grow(x, y): # x = numeric values; y = class labels

-    root = Node()

-    root.split_value = bestsplit(x, y)

-    root.left = Leaf(0)

-    root.right = Leaf(1)

-    return root

-    

-def tree_pred(x, tr):

-    y = []

-    for value in x:

-        y.append(single_value_pred(value, tr))  

-    return y

-

-def single_value_pred(value, current_tree):

-    if isinstance(current_tree, Leaf):

-        return current_tree.predicted_class

-    else:

-        if value <= current_tree.split_value:

-            return single_value_pred(value, current_tree.left)

-        else:

-            return single_value_pred(value, current_tree.right)        

-

-tree = tree_grow(credit_data[:,3], credit_data[:,5])

-print(tree_pred([32, 38, 3, 40], tree))

-

-

-