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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