Programming questions

from csv import reader # reader object reads a csv file line by line from random import seed # seeds the random number generator from random import randrange # returns a random value in a specified range from Perceptron import Perceptron # this is the Perceptron class in the Perceptron.py file ###################################################################### ##### DATASET FUNCTIONS ##### ###################################################################### # Load the CSV file containing the inputs and desired outputs # # dataset is a 2D matrix where each row contains 1 set of inputs plus the desired output # -for each row, columns 0-59 contain the inputs as floating point values # -column 60 contains the desired output as a character: 'R' for Rock or 'M' for Metal # -all values will be string values; conversion to appropriate types will be necessary # -no bias value is included in the data file def load_csv(filename): # dataset will be the matrix containing the inputs dataset = list() # Standard Python code to read each line of text from the file as a row with open(filename, 'r') as file: csv_reader = reader(file) for row in csv_reader: if not row: continue # add current row to dataset dataset.append(row) return dataset # Convert the input values in the specified column of the dataset from strings to floats def convert_inputs_to_float(dataset, column): for row in dataset: row[column] = float(row[column].strip()) # Convert the desired output values, located in the specified column, to unique integers # For 2 classes of outputs, 1 desired output will be 0, the other will be 1 def convert_desired_outputs_to_int(dataset, column): # Enumerate all the values in the specified column for each row class_values = [row[column] for row in dataset] # Create a set containing only the unique values unique = set(class_values) # Create a lookup table to map each unique value to an integer (either 0 or 1) lookup = dict() for i, value in enumerate(unique): lookup[value] = i # Replace the desired output string values with the corresponding integer values for row in dataset: row[column] = lookup[row[column]] return lookup # Load the dataset from the CSV file specified by filename def load_dataset(filename): # Read the data from the specified file dataset = load_csv(filename) # Convert all the input values form strings to floats for column in range(len(dataset[0])-1): convert_inputs_to_float(dataset, column) # Convert the desired outputs from strings to ints convert_desired_outputs_to_int(dataset, len(dataset[0]) - 1) ###################################################################### ##### CREATE THE TRAINING SET ##### ###################################################################### # Create the training set # -Training set will consist of the specified percent fraction of the dataset # -How many inputs you decide to use for the training set, and how you choose # those values, is entirely up to you # # Params: dataset - the entire dataset # # Returns: a matrix, or list of rows, containing only a subset of the input # vectors from the entire dataset def create_training_set(dataset): ###################################################################### ##### CREATE A PERCEPTRON, TRAIN IT, AND TEST IT ##### ###################################################################### # Step 1: Acquire the dataset dataset = load_csv('sonar_all-data.csv') # Step 2: Convert the string input values to floats # Step 3: Convert the desired outputs to int values # Step 4: Create the training set # Step 5: Create the perceptron # Step 6: Train the perceptron # Step 7: Test the trained perceptron # Step 8: Display the test results and accuracy of the perceptron