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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): |
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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) | |
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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) |
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