The AI must predict the next number in a given sequence of incremental integers (with no obvious pattern) using Python but so far I don't get the intended result! I tried changing the learning rate and iterations but so far no luck!
Example sequence: [1, 3, 7, 8, 21, 49, 76, 224]
Expected result: 467
Result found : 2,795.5
Cost: 504579.43
PS. The same thread exists on AI Stackexchange & Stackoverflow and I've been advised to post it here!
This is what I've done so far:
import numpy as np
# Init sequence
data =\
[
[0, 1.0], [1, 3.0], [2, 7.0], [3, 8.0],
[4, 21.0], [5, 49.0], [6, 76.0], [7, 224.0]
]
X = np.matrix(data)[:, 0]
y = np.matrix(data)[:, 1]
def J(X, y, theta):
theta = np.matrix(theta).T
m = len(y)
predictions = X * theta
sqError = np.power((predictions-y), [2])
return 1/(2*m) * sum(sqError)
dataX = np.matrix(data)[:, 0:1]
X = np.ones((len(dataX), 2))
X[:, 1:] = dataX
# gradient descent function
def gradient(X, y, alpha, theta, iters):
J_history = np.zeros(iters)
m = len(y)
theta = np.matrix(theta).T
for i in range(iters):
h0 = X * theta
delta = (1 / m) * (X.T * h0 - X.T * y)
theta = theta - alpha * delta
J_history[i] = J(X, y, theta.T)
return J_history, theta
print('\n'+40*'=')
# Theta initialization
theta = np.matrix([np.random.random(), np.random.random()])
# Learning rate
alpha = 0.02
# Iterations
iters = 1000000
print('\n== Model summary ==\nLearning rate: {}\nIterations: {}\nInitial
theta: {}\nInitial J: {:.2f}\n'
.format(alpha, iters, theta, J(X, y, theta).item()))
print('Training model... ')
# Train model and find optimal Theta value
J_history, theta_min = gradient(X, y, alpha, theta, iters)
print('Done, Model is trained')
print('\nModelled prediction function is:\ny = {:.2f} * x + {:.2f}'
.format(theta_min[1].item(), theta_min[0].item()))
print('Cost is: {:.2f}'.format(J(X, y, theta_min.T).item()))
# Calculate the predicted profit
def predict(pop):
return [1, pop] * theta_min
# Now
p = len(data)
print('\n'+40*'=')
print('Initial sequence was:\n', *np.array(data)[:, 1])
print('\nNext numbers should be: {:,.1f}'
.format(predict(p).item()))
Another method I tried but still giving wrong results
import numpy as np
from sklearn import datasets, linear_model
# Define the problem
problem = [1, 3, 7, 8, 21, 49, 76, 224]
# create x and y for the problem
x = []
y = []
for (xi, yi) in enumerate(problem):
x.append([xi])
y.append(yi)
x = np.array(x)
y = np.array(y)
# Create linear regression object
regr = linear_model.LinearRegression()
regr.fit(x, y)
# create the testing set
x_test = [[i] for i in range(len(x), 3 + len(x))]
# The coefficients
print('Coefficients: \n', regr.coef_)
# The mean squared error
print("Mean squared error: %.2f" % np.mean((regr.predict(x) - y) ** 2))
# Explained variance score: 1 is perfect prediction
print('Variance score: %.2f' % regr.score(x, y))
# Do predictions
y_predicted = regr.predict(x_test)
print("Next few numbers in the series are")
for pred in y_predicted:
print(pred)
