Laurae2’s gists

Laurae2 / linear_regression.R

Last active February 24, 2017 19:02

Linear Regression demo using R

	# Setting up random matrix
	data <- as.matrix(data.frame(Intercept = rep(1, 6),
	a = c(3, 4, 5, 6, 7, 8),
	b = c(1, 4, 3, 7, 10, 12),
	c = c(6, 5, 1, 9, 18, 21)))

	# Setting up the (perfect) linear relationship
	preds <- 2 + (data[, 2] * 2) + (data[, 3] * 3) + (data[, 4] * 4)

	# Plotting data to understand what we have

Laurae2 / linear_regression_gradient.R

Created February 24, 2017 14:51

Gradient Descent for linear regression demo.

	# Generate random values
	set.seed(11111)
	x <- rnorm(n = 50)
	y <- 10 * x + rnorm(n = 50)

	# Add intercept
	x <- cbind(1,x)

	# Assume random null parameters
	param <- c(0,0)

Laurae2 / linear_regression_gradient_plot.R

Created February 24, 2017 15:00

Gradient Descent levelplot for linear regression

	# Generate random values
	set.seed(11111)
	x <- rnorm(n = 50)
	y <- 10 * x + rnorm(n = 50)

	# Add intercept
	x <- cbind(1,x)

	# Assume random null parameters
	param <- c(0,0)

Laurae2 / polynomial_regression.R

Created February 24, 2017 19:03

Polynomial Regression demo using R

	# Setting up random matrix
	set.seed(11111)
	data <- data.frame(a = rnorm(n = 15) * 5,
	b = rnorm(n = 15) * 3 + 1,
	c = rnorm(n = 15) * 2 + 2)

	# Setting up the (perfect) linear relationship
	preds <- 2 + (data[, 1] * 2) + (data[, 2] * 3) + (data[, 3] * 4) + (data[, 3] ^ 2) + (data[, 1] * data[, 2])

	# Setting up polynomial features

Laurae2 / polynomial_exploding_gradient.R

Last active February 25, 2017 12:26

Polynomial Regression with Exploding Gradient in R

	# Setting up random matrix
	set.seed(11111)
	x <- data.frame(a = rnorm(n = 15) * 5,
	b = rnorm(n = 15) * 3 + 1,
	c = rnorm(n = 15) * 2 + 2)

	# Setting up the (perfect) linear relationship
	y <- 2 + (x[, 1] * 2) + (x[, 2] * 3) + (x[, 3] * 4) + (x[, 3] ^ 2) + (x[, 1] * x[, 2])

	# Setting up polynomial features

Laurae2 / aic_bic.R

Created February 24, 2017 19:55

Akaike Information Criterion and Bayesian Information Criterion

	lm_model <- lm(preds ~ ., data = data)

	my_AIC <- AIC(lm_model)
	my_AIC <- nrow(data) * (log(2 * pi) + 1 + log((sum(lm_model$residuals ^ 2) / nrow(data)))) + ((length(lm_model$coefficients) + 1) * 2)
	my_BIC <- AIC(lm_model, k = log(nrow(data)))
	my_BIC <- nrow(data) * (log(2 * pi) + 1 + log((sum(lm_model$residuals ^ 2) / nrow(data)))) + ((length(lm_model$coefficients) + 1) * log(nrow(data)))

Laurae2 / stepwise_regression_forward.R

Created February 24, 2017 20:55

Forward Stepwise Regression in R without step/lm

	# Setting up random matrix
	set.seed(11111)
	data <- data.frame(a = rnorm(n = 15) * 5,
	b = rnorm(n = 15) * 3 + 1,
	c = rnorm(n = 15) * 2 + 2)

	# Setting up the (perfect) linear relationship
	preds <- 2 + (data[, 1] * 2) + (data[, 2] * 3) + (data[, 3] * 4) + (data[, 3] ^ 2) + (data[, 1] * data[, 2])

	# Setting up polynomial features

Laurae2 / L1_regularization_regression.R

Last active February 25, 2017 20:21

L1 Regularizaion Regression example in R

	# Setting up random matrix
	set.seed(11111)
	x <- data.frame(a = rnorm(n = 15) * 5,
	b = rnorm(n = 15) * 3 + 1,
	c = rnorm(n = 15) * 2 + 2)

	# Setting up the (perfect) linear relationship
	y <- 2 + (x[, 1] * 2) + (x[, 2] * 3) + (x[, 3] * 4) + (x[, 3] ^ 2) + (x[, 1] * x[, 2])

	# Setting up polynomial features

Laurae2 / L2_regularization_regression.R

Last active February 25, 2017 20:20

L2 Regularizaion Regression example in R

	# Setting up random matrix
	set.seed(11111)
	x <- data.frame(a = rnorm(n = 15) * 5,
	b = rnorm(n = 15) * 3 + 1,
	c = rnorm(n = 15) * 2 + 2)

	# Setting up the (perfect) linear relationship
	y <- 2 + (x[, 1] * 2) + (x[, 2] * 3) + (x[, 3] * 4) + (x[, 3] ^ 2) + (x[, 1] * x[, 2])

	# Setting up polynomial features

Laurae2 / elastic_net.R

Last active February 25, 2017 20:19

Elastic Net Regularizaion example in R

	# Setting up random matrix
	set.seed(11111)
	x <- data.frame(a = rnorm(n = 15) * 5,
	b = rnorm(n = 15) * 3 + 1,
	c = rnorm(n = 15) * 2 + 2)

	# Setting up the (perfect) linear relationship
	y <- 2 + (x[, 1] * 2) + (x[, 2] * 3) + (x[, 3] * 4) + (x[, 3] ^ 2) + (x[, 1] * x[, 2])

	# Setting up polynomial features

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