gkhayes

import numpy as np
import gym
import matplotlib.pyplot as plt

# Import and initialize Mountain Car Environment
env = gym.make('MountainCar-v0')
env.reset()

# Define Q-learning function
def QLearning(env, learning, discount, epsilon, min_eps, episodes):

gkhayes

import gym

env = gym.make('CartPole-v0')
env.reset()

for _ in range(1000):
    env.render()
    env.step(env.action_space.sample())
    
env.close()

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# Initialize logistic regression object and fit object
lr_model2 = mlrose.LogisticRegression(algorithm = 'random_hill_climb', max_iters = 1000,
                                      bias = True, learning_rate = 0.01,
                                      early_stopping = True, clip_max = 5, max_attempts = 100,
                                      random_state = 3)

lr_model2.fit(X_train_scaled, y_train_hot)

# Predict labels for train set and assess accuracy
y_train_pred = lr_model2.predict(X_train_scaled)

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# Initialize logistic regression object and fit object
lr_model1 = mlrose.LogisticRegression(algorithm = 'random_hill_climb', max_iters = 1000,
                                      bias = True, learning_rate = 0.0001,
                                      early_stopping = True, clip_max = 5, max_attempts = 100,
                                      random_state = 3)

lr_model1.fit(X_train_scaled, y_train_hot)

# Predict labels for train set and assess accuracy
y_train_pred = lr_model1.predict(X_train_scaled)

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lr_nn_model1 = mlrose.NeuralNetwork(hidden_nodes = [], activation = 'sigmoid',
                                    algorithm = 'random_hill_climb', max_iters = 1000,
                                    bias = True, is_classifier = True, learning_rate = 0.0001,
                                    early_stopping = True, clip_max = 5, max_attempts = 100,
                                    random_state = 3)

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# Initialize neural network object and fit object
nn_model2 = mlrose.NeuralNetwork(hidden_nodes = [2], activation = 'relu',
                                 algorithm = 'gradient_descent', max_iters = 1000,
                                 bias = True, is_classifier = True, learning_rate = 0.0001,
                                 early_stopping = True, clip_max = 5, max_attempts = 100,
                                 random_state = 3)

nn_model2.fit(X_train_scaled, y_train_hot)

# Predict labels for train set and assess accuracy

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from sklearn.metrics import accuracy_score
	
# Predict labels for train set and assess accuracy
y_train_pred = nn_model1.predict(X_train_scaled)

y_train_accuracy = accuracy_score(y_train_hot, y_train_pred)

print('Training accuracy: ', y_train_accuracy)

# Predict labels for test set and assess accuracy

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# Initialize neural network object and fit object
nn_model1 = mlrose.NeuralNetwork(hidden_nodes = [2], activation = 'relu',
                                 algorithm = 'random_hill_climb', max_iters = 1000,
                                 bias = True, is_classifier = True, learning_rate = 0.0001,
                                 early_stopping = True, clip_max = 5, max_attempts = 100,
				 random_state = 3)

nn_model1.fit(X_train_scaled, y_train_hot)
	

gkhayes

from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler, OneHotEncoder

# Split data into training and test sets
X_train, X_test, y_train, y_test = train_test_split(data.data, data.target,
                                                    test_size = 0.2, random_state = 3)

# Normalize feature data
scaler = MinMaxScaler()

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from sklearn.datasets import load_iris
    
# Load the Iris dataset
data = load_iris()

# Get feature values
print('The feature values for Obs 0 are: ', data.data[0])

# Get feature names
print('The feature names are: ', data.feature_names)