minor bug fixes and more unit tests.

.travis.yml

@@ -5,9 +5,12 @@ notifications:
   email: false
 before_install:
   - sudo add-apt-repository ppa:staticfloat/julia-deps -y
-  - sudo add-apt-repository ppa:staticfloat/julianightlies -y
+  - sudo add-apt-repository ppa:staticfloat/juliareleases -y
   - sudo apt-get update -qq -y
   - sudo apt-get install libpcre3-dev julia -y
 script:
   - julia -e 'Pkg.init(); run(`ln -s $(pwd()) $(Pkg.dir("LinearLeastSquares"))`); Pkg.pin("LinearLeastSquares"); Pkg.resolve()'
   - julia -e 'using LinearLeastSquares; @assert isdefined(:LinearLeastSquares); @assert typeof(LinearLeastSquares) === Module'
+  - julia --code-coverage test/runtests.jl
+after_success:
+  - julia -e 'cd(Pkg.dir("LinearLeastSquares")); Pkg.add("Coverage"); using Coverage; Coveralls.submit(Coveralls.process_folder())'

REQUIRE

@@ -1,2 +1,2 @@
-julia 0.2+
+julia 0.3
 

examples/regression/data.jl

@@ -0,0 +1,22 @@
+using Gadfly
+
+# Set the random seed to get consistent data
+srand(1)
+
+# Number of examples to use
+n = 100
+
+# Specify the true value of the variable
+true_coeffs = [2; -2; 0.5];
+
+# Generate data
+x_data = rand(n, 1) * 5;
+x_data_expanded = hcat([x_data .^ i for i in 1 : 3]...)
+y_data = x_data_expanded * true_coeffs + 0.5 * rand(n, 1)
+
+
+p = plot(
+  x=x_data, y=y_data, Geom.point,
+  Theme(panel_fill=color("white"))
+)
+draw(PNG("data.png", 16cm, 12cm), p)

examples/regression/linear_regression.jl

@@ -0,0 +1,35 @@
+# Simple Linear Regression
+# Originally written by Keegan Go for lsqpy
+# Translated into LinearLeastSquares.jl by Karanveer Mohan and David Zeng
+
+using LinearLeastSquares
+using Gadfly
+# Set the random seed to get consistent data
+srand(1)
+
+# Number of examples to use
+n = 100
+
+# Specify the true value of the variable
+true_coeffs = [2; -2; 0.5]
+
+# Generate data
+x_data = rand(n) * 5
+x_data_expanded = hcat([x_data .^ i for i in 1 : 3]...)
+y_data = x_data_expanded * true_coeffs + 0.5 * rand(n)
+
+slope = Variable()
+offset = Variable()
+line = offset + x_data * slope
+residuals = line - y_data
+error = sum_squares(residuals)
+optval = minimize!(error)
+
+# plot the data and the line
+t = [0; 5; 0.1]
+p = plot(
+  layer(x=x_data, y=y_data, Geom.point),
+  layer(x=t, y=evaluate(slope) * t + evaluate(offset), Geom.line),
+  Theme(panel_fill=color("white"))
+)
+# draw(PNG("linear_regression.png", 16cm, 12cm), p)

examples/simple_lin_and_quad_reg/quadratic_regression.jl → examples/regression/quadratic_regression.jl

@@ -3,35 +3,37 @@
 # Translated into LinearLeastSquares.jl by Karanveer Mohan and David Zeng
 
 using LinearLeastSquares
+using Gadfly
 # Set the random seed to get consistent data
 srand(1)
 
 # Number of examples to use
 n = 100
 
 # Specify the true value of the variable
-true_coeffs = [2; -2; 0.5];
+true_coeffs = [2; -2; 0.5]
 
 # Generate data
 x_data = rand(n, 1) * 5
 x_data_expanded = hcat([x_data .^ i for i in 1 : 3]...)
 y_data = x_data_expanded * true_coeffs + 0.5 * rand(n, 1)
 
-quadratic_coeff = Variable();
-slope = Variable();
-offset = Variable();
-quadratic = offset + x_data * slope + quadratic_coeff * x_data .^ 2;
-residuals = quadratic - y_data;
-error = sum_squares(residuals);
-optval = minimize!(error);
+quadratic_coeff = Variable()
+slope = Variable()
+offset = Variable()
+quadratic = offset + x_data * slope + quadratic_coeff * x_data .^ 2
+residuals = quadratic - y_data
+error = sum_squares(residuals)
+optval = minimize!(error)
 
 # Create some evenly spaced points for plotting, again replicate powers
-t = reshape([0 : 0.1 : 5], length([0 : 0.1 : 5]), 1);
-t_squared = t .^ 2;
+t = reshape([0 : 0.1 : 5], length([0 : 0.1 : 5]), 1)
+t_squared = t .^ 2
 
 # Plot our regressed function
-plt.plot(x_data, y_data, "ro")
-plt.plot(t, evaluate(offset) + t * evaluate(slope) + t_squared * evaluate(quadratic_coeff), "b")
-plt.xlabel("x")
-plt.ylabel("y")
-plt.show()
+p = plot(
+  layer(x=x_data, y=y_data, Geom.point),
+  layer(x=t, y=evaluate(offset) + t * evaluate(slope) + t_squared * evaluate(quadratic_coeff), Geom.line),
+  Theme(panel_fill=color("white"))
+)
+# draw(PNG("quadratic_regression.png", 16cm, 12cm), p)