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tc_linefit.rb
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tc_linefit.rb
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require_relative "lib/linefit"
require "test/unit"
class TestLineFit < Test::Unit::TestCase
def setup
@lineFit = LineFit.new
end
def test_basic_example
x = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18]
y = [4039,4057,4052,4094,4104,4110,4154,4161,4186,4195,4229,4244,4242,4283,4322,4333,4368,4389]
@lineFit.setData(x, y)
intercept, slope = @lineFit.coefficients
rSquared = @lineFit.rSquared
meanSquaredError = @lineFit.meanSqError
durbinWatson = @lineFit.durbinWatson
sigma = @lineFit.sigma
tStatIntercept, tStatSlope = @lineFit.tStatistics
predictedYs = @lineFit.predictedYs
residuals = @lineFit.residuals
varianceIntercept, varianceSlope = @lineFit.varianceOfEstimates
assert_equal(4002.0653594771243, intercept, 'Intercept calc error')
assert_equal(20.613003095975234, slope, 'Slope calc error')
assert_equal(0.9871028869331805, rSquared, 'rSquared calc error')
assert_equal(149.42865879368685, meanSquaredError, 'Mean Squared Error calc error')
assert_equal(1.5273464901323115, durbinWatson, 'Durbin Watson calc error')
assert_equal(12.965617653737045, sigma, 'Sigma calc error')
assert_equal(627.6764289396855, tStatIntercept, 't Stat Intercept calc error')
assert_equal(34.99410968577564, tStatSlope, 't Stat Slope calc error')
correct_predicted_ys = [
4022.6783625730995,
4043.291365669075,
4063.90436876505,
4084.5173718610254,
4105.130374957001,
4125.7433780529755,
4146.356381148951,
4166.969384244926,
4187.582387340902,
4208.195390436877,
4228.808393532852,
4249.421396628827,
4270.034399724802,
4290.647402820778,
4311.2604059167525,
4331.873409012728,
4352.486412108703,
4373.099415204679]
assert_equal(correct_predicted_ys, predictedYs, 'Predicted Ys calc error')
correct_residuals = [
16.321637426900452,
13.708634330925179,
-11.904368765050094,
9.482628138974633,
-1.1303749570006403,
-15.743378052975459,
7.643618851048814,
-5.969384244926005,
-1.5823873409017324,
-13.19539043687655,
0.19160646714772156,
-5.421396628827097,
-28.034399724801915,
-7.647402820777643,
10.739594083247539,
1.1265909872718112,
15.513587891296993,
15.900584795321265]
assert_equal(correct_residuals, residuals, 'Residuals calc error')
assert_equal(2.3146946263794774, varianceIntercept, 'Variance Intercept calc error')
assert_equal(0.01905749720682022, varianceSlope, 'Variance Slope calc error')
newX = 24
newY = @lineFit.forecast(newX)
assert_equal(4496.7774337805295, newY, 'Linefit forecast failed')
end
def test_weighted_example
x = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18]
y = [4039,4057,4052,4094,4104,4110,4154,4161,4186,4195,4229,4244,4242,4283,4322,4333,4368,4389]
weights = [5,10,0,5,10,10,20,5,0,10,5,5,5,10,20,0,5,10]
@lineFit.setData(x, y, weights)
intercept, slope = @lineFit.coefficients
rSquared = @lineFit.rSquared
meanSquaredError = @lineFit.meanSqError
durbinWatson = @lineFit.durbinWatson
sigma = @lineFit.sigma
tStatIntercept, tStatSlope = @lineFit.tStatistics
predictedYs = @lineFit.predictedYs
residuals = @lineFit.residuals
varianceIntercept, varianceSlope = @lineFit.varianceOfEstimates
assert_equal(4003.280090351728, intercept, 'Intercept calc error')
assert_equal(20.713778638270224, slope, 'Slope calc error')
assert_equal(0.9871368644064613, rSquared, 'rSquared calc error')
assert_equal(142.60456777917958, meanSquaredError, 'Mean Squared Error calc error')
assert_equal(1.600535293064521, durbinWatson, 'Durbin Watson calc error')
assert_equal(12.666101955675908, sigma, 'Sigma calc error')
assert_equal(615.3750790554614, tStatIntercept, 't Stat Intercept calc error')
assert_equal(35.04090005949416, tStatSlope, 't Stat Slope calc error')
correct_predicted_ys = [
4023.993868989998,
4044.7076476282687,
4065.4214262665387,
4086.135204904809,
4106.848983543079,
4127.562762181349,
4148.27654081962,
4168.99031945789,
4189.70409809616,
4210.41787673443,
4231.131655372701,
4251.845434010971,
4272.559212649241,
4293.272991287511,
4313.986769925781,
4334.700548564052,
4355.414327202322,
4376.128105840592]
assert_equal(correct_predicted_ys, predictedYs, 'Predicted Ys calc error')
correct_residuals = [
15.006131010001809,
12.29235237173134,
-13.421426266538674,
7.864795095190857,
-2.8489835430791572,
-17.56276218134917,
5.723459180379905,
-7.990319457890109,
-3.7040980961601235,
-15.417876734430138,
-2.1316553727010614,
-7.845434010971076,
-30.55921264924109,
-10.272991287511104,
8.013230074218882,
-1.700548564052042,
12.585672797677944,
12.87189415940793]
assert_equal(correct_residuals, residuals, 'Residuals calc error')
assert_equal(2.1157840392583114, varianceIntercept, 'Variance Intercept calc error')
assert_equal(0.04268092032760079, varianceSlope, 'Variance Slope calc error')
newX = 24
newY = @lineFit.forecast(newX)
assert_equal(4500.410777670213, newY, 'Linefit forecast failed')
end
def test_basic_example_with_array_of_arrays
xy = [
[ 1, 4039],
[ 2, 4057],
[ 3, 4052],
[ 4, 4094],
[ 5, 4104],
[ 6, 4110],
[ 7, 4154],
[ 8, 4161],
[ 9, 4186],
[10, 4195],
[11, 4229],
[12, 4244],
[13, 4242],
[14, 4283],
[15, 4322],
[16, 4333],
[17, 4368],
[18, 4389] ]
@lineFit.setData(xy)
intercept, slope = @lineFit.coefficients
rSquared = @lineFit.rSquared
meanSquaredError = @lineFit.meanSqError
durbinWatson = @lineFit.durbinWatson
sigma = @lineFit.sigma
tStatIntercept, tStatSlope = @lineFit.tStatistics
predictedYs = @lineFit.predictedYs
residuals = @lineFit.residuals
varianceIntercept, varianceSlope = @lineFit.varianceOfEstimates
assert_equal(4002.0653594771243, intercept, 'Intercept calc error')
assert_equal(20.613003095975234, slope, 'Slope calc error')
assert_equal(0.9871028869331805, rSquared, 'rSquared calc error')
assert_equal(149.42865879368685, meanSquaredError, 'Mean Squared Error calc error')
assert_equal(1.5273464901323115, durbinWatson, 'Durbin Watson calc error')
assert_equal(12.965617653737045, sigma, 'Sigma calc error')
assert_equal(627.6764289396855, tStatIntercept, 't Stat Intercept calc error')
assert_equal(34.99410968577564, tStatSlope, 't Stat Slope calc error')
correct_predicted_ys = [
4022.6783625730995,
4043.291365669075,
4063.90436876505,
4084.5173718610254,
4105.130374957001,
4125.7433780529755,
4146.356381148951,
4166.969384244926,
4187.582387340902,
4208.195390436877,
4228.808393532852,
4249.421396628827,
4270.034399724802,
4290.647402820778,
4311.2604059167525,
4331.873409012728,
4352.486412108703,
4373.099415204679]
assert_equal(correct_predicted_ys, predictedYs, 'Predicted Ys calc error')
correct_residuals = [
16.321637426900452,
13.708634330925179,
-11.904368765050094,
9.482628138974633,
-1.1303749570006403,
-15.743378052975459,
7.643618851048814,
-5.969384244926005,
-1.5823873409017324,
-13.19539043687655,
0.19160646714772156,
-5.421396628827097,
-28.034399724801915,
-7.647402820777643,
10.739594083247539,
1.1265909872718112,
15.513587891296993,
15.900584795321265]
assert_equal(correct_residuals, residuals, 'Residuals calc error')
assert_equal(2.3146946263794774, varianceIntercept, 'Variance Intercept calc error')
assert_equal(0.01905749720682022, varianceSlope, 'Variance Slope calc error')
newX = 24
newY = @lineFit.forecast(newX)
assert_equal(4496.7774337805295, newY, 'Linefit forecast failed')
end
def test_array_of_arrays_with_weighted_example
xy = [
[ 1, 4039],
[ 2, 4057],
[ 3, 4052],
[ 4, 4094],
[ 5, 4104],
[ 6, 4110],
[ 7, 4154],
[ 8, 4161],
[ 9, 4186],
[10, 4195],
[11, 4229],
[12, 4244],
[13, 4242],
[14, 4283],
[15, 4322],
[16, 4333],
[17, 4368],
[18, 4389] ]
weights = [5,10,0,5,10,10,20,5,0,10,5,5,5,10,20,0,5,10]
@lineFit.setData(xy, weights)
intercept, slope = @lineFit.coefficients
rSquared = @lineFit.rSquared
meanSquaredError = @lineFit.meanSqError
durbinWatson = @lineFit.durbinWatson
sigma = @lineFit.sigma
tStatIntercept, tStatSlope = @lineFit.tStatistics
predictedYs = @lineFit.predictedYs
residuals = @lineFit.residuals
varianceIntercept, varianceSlope = @lineFit.varianceOfEstimates
assert_equal(4003.280090351728, intercept, 'Intercept calc error')
assert_equal(20.713778638270224, slope, 'Slope calc error')
assert_equal(0.9871368644064613, rSquared, 'rSquared calc error')
assert_equal(142.60456777917958, meanSquaredError, 'Mean Squared Error calc error')
assert_equal(1.600535293064521, durbinWatson, 'Durbin Watson calc error')
assert_equal(12.666101955675908, sigma, 'Sigma calc error')
assert_equal(615.3750790554614, tStatIntercept, 't Stat Intercept calc error')
assert_equal(35.04090005949416, tStatSlope, 't Stat Slope calc error')
correct_predicted_ys = [
4023.993868989998,
4044.7076476282687,
4065.4214262665387,
4086.135204904809,
4106.848983543079,
4127.562762181349,
4148.27654081962,
4168.99031945789,
4189.70409809616,
4210.41787673443,
4231.131655372701,
4251.845434010971,
4272.559212649241,
4293.272991287511,
4313.986769925781,
4334.700548564052,
4355.414327202322,
4376.128105840592]
assert_equal(correct_predicted_ys, predictedYs, 'Predicted Ys calc error')
correct_residuals = [
15.006131010001809,
12.29235237173134,
-13.421426266538674,
7.864795095190857,
-2.8489835430791572,
-17.56276218134917,
5.723459180379905,
-7.990319457890109,
-3.7040980961601235,
-15.417876734430138,
-2.1316553727010614,
-7.845434010971076,
-30.55921264924109,
-10.272991287511104,
8.013230074218882,
-1.700548564052042,
12.585672797677944,
12.87189415940793]
assert_equal(correct_residuals, residuals, 'Residuals calc error')
assert_equal(2.1157840392583114, varianceIntercept, 'Variance Intercept calc error')
assert_equal(0.04268092032760079, varianceSlope, 'Variance Slope calc error')
newX = 24
newY = @lineFit.forecast(newX)
assert_equal(4500.410777670213, newY, 'Linefit forecast failed')
end
end