How to use xys method of Minitest Package

Best Minitest_ruby code snippet using Minitest.xys

benchmark.rb

Source:benchmark.rb

...186 ##187 # Takes an array of x/y pairs and calculates the general R^2 value.188 #189 # See: http://en.wikipedia.org/wiki/Coefficient_of_determination190 def fit_error xys191 y_bar = sigma(xys) { |_, y| y } / xys.size.to_f192 ss_tot = sigma(xys) { |_, y| (y - y_bar) ** 2 }193 ss_err = sigma(xys) { |x, y| (yield(x) - y) ** 2 }194 1 - (ss_err / ss_tot)195 end196 ##197 # To fit a functional form: y = ae^(bx).198 #199 # Takes x and y values and returns [a, b, r^2].200 #201 # See: http://mathworld.wolfram.com/LeastSquaresFittingExponential.html202 def fit_exponential xs, ys203 n = xs.size204 xys = xs.zip(ys)205 sxlny = sigma(xys) { |x, y| x * Math.log(y) }206 slny = sigma(xys) { |_, y| Math.log(y) }207 sx2 = sigma(xys) { |x, _| x * x }208 sx = sigma xs209 c = n * sx2 - sx ** 2210 a = (slny * sx2 - sx * sxlny) / c211 b = ( n * sxlny - sx * slny ) / c212 return Math.exp(a), b, fit_error(xys) { |x| Math.exp(a + b * x) }213 end214 ##215 # To fit a functional form: y = a + b*ln(x).216 #217 # Takes x and y values and returns [a, b, r^2].218 #219 # See: http://mathworld.wolfram.com/LeastSquaresFittingLogarithmic.html220 def fit_logarithmic xs, ys221 n = xs.size222 xys = xs.zip(ys)223 slnx2 = sigma(xys) { |x, _| Math.log(x) ** 2 }224 slnx = sigma(xys) { |x, _| Math.log(x) }225 sylnx = sigma(xys) { |x, y| y * Math.log(x) }226 sy = sigma(xys) { |_, y| y }227 c = n * slnx2 - slnx ** 2228 b = ( n * sylnx - sy * slnx ) / c229 a = (sy - b * slnx) / n230 return a, b, fit_error(xys) { |x| a + b * Math.log(x) }231 end232 ##233 # Fits the functional form: a + bx.234 #235 # Takes x and y values and returns [a, b, r^2].236 #237 # See: http://mathworld.wolfram.com/LeastSquaresFitting.html238 def fit_linear xs, ys239 n = xs.size240 xys = xs.zip(ys)241 sx = sigma xs242 sy = sigma ys243 sx2 = sigma(xs) { |x| x ** 2 }244 sxy = sigma(xys) { |x, y| x * y }245 c = n * sx2 - sx**2246 a = (sy * sx2 - sx * sxy) / c247 b = ( n * sxy - sx * sy ) / c248 return a, b, fit_error(xys) { |x| a + b * x }249 end250 ##251 # To fit a functional form: y = ax^b.252 #253 # Takes x and y values and returns [a, b, r^2].254 #255 # See: http://mathworld.wolfram.com/LeastSquaresFittingPowerLaw.html256 def fit_power xs, ys257 n = xs.size258 xys = xs.zip(ys)259 slnxlny = sigma(xys) { |x, y| Math.log(x) * Math.log(y) }260 slnx = sigma(xs) { |x | Math.log(x) }261 slny = sigma(ys) { | y| Math.log(y) }262 slnx2 = sigma(xs) { |x | Math.log(x) ** 2 }263 b = (n * slnxlny - slnx * slny) / (n * slnx2 - slnx ** 2)264 a = (slny - b * slnx) / n265 return Math.exp(a), b, fit_error(xys) { |x| (Math.exp(a) * (x ** b)) }266 end267 ##268 # Enumerates over +enum+ mapping +block+ if given, returning the269 # sum of the result. Eg:270 #271 # sigma([1, 2, 3]) # => 1 + 2 + 3 => 6272 # sigma([1, 2, 3]) { |n| n ** 2 } # => 1 + 4 + 9 => 14273 def sigma enum, &block274 enum = enum.map(&block) if block275 enum.inject { |sum, n| sum + n }276 end277 ##278 # Returns a proc that calls the specified fit method and asserts279 # that the error is within a tolerable threshold....

xys

Using AI Code Generation

1 assert_equal(4, M.new.xys(2, 2))2 def xys(x, y)3 assert_equal(4, M.new.xys(2, 2))4 def xys(x, y)5 assert_equal(4, M.new.xys(2, 2))6 def xys(x, y)7 assert_equal(4, M.new.xys(2, 2))8 def xys(x, y)

xys

Using AI Code Generation

1 assert_equal('xyz', xys)2 assert_equal('xyz', xys)3 assert_equal('xyz', xys)4 assert_equal('xyz',

xys

Using AI Code Generation

1ruby 2.2.2p95 (2015-04-13 revision 50295) [x86_64-linux]2minitest (5.4.3, 4.7.5)3rake (10.4.2, 10.4.1, 10.4.0, 10.3.2, 10.3.1, 10.3.0, 10.2.2, 10.2.1, 10.2.0, 10.1.1, 10.1.0, 10.0.4, 10.0.3, 10.0.2, 10.0.1, 10.0.0, 9.2.2, 9.2.1, 9.2.0, 9.1.7, 9.1.6, 9.1.5, 9.1.4, 9.1.3, 9.1.2, 9.1.1, 9.1.0, 9.0.3, 9.0.2, 9.0.1, 9.0.0, 8

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