nested_fit/nf_input.yaml at master · martinit18/nested_fit · GitHub

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version:    5.0
datafiles:  quadratic.dat
specstr:    x,c,ce
# specstr: x,c
likelihood: GAUSSIAN

function:
  expression: f(x, a, b) = ax + b
  # expression_1: f(x, a, b, c, d) = \texttt{Quadratic}(x, a, b, c, d)
  # expression_2: g(x, a, b, c) = \texttt{Quadratic}(x, a, b, c, 1.0)
  # expression_3: h(x, a, b, e_a) = \texttt{Quadratic}(x, a, b, 1.0, e_a)
  params:
    a: { value: 0, step: -1, min: 0, max: 10 }
    b: { value: 0, step: -1, min: 0, max: 10 }
    c: { value: 0, step: -1, min: 0, max: 10 }
    d: { value: 0, step: -1, min: 0, max: 10 }
    e_a:
      value: 0
      step: -1
      min: 0
      max: 10
      fixed: false

data: { xmin: 1, xmax: 100, ymin: 0, ymax: 0 }
# data_1: { xmin: 1, xmax: 99, ymin: 0, ymax: 0 }
# data_2: { xmin: 1, xmax: 99, ymin: 0, ymax: 0 }
# data_3: { xmin: 1, xmax: 99, ymix: 0, ymax: 0 }

search:
  livepoints: 200
  method:     RANDOM_WALK
  param1:     0.2
  param2:     20
  max_tries:  1000
  tries_mult: 100
  num_tries:  1
  max_steps:  100000

convergence:
  method:    LIKE_ACC
  accuracy:  1.E-05
  parameter: 0.01

clustering:
  enabled:   false
  # method:    f
  # distance:  0.5
  # bandwidth: 0.2

# Legacy stuff
lr: L
npoint: 500
nwidth: 20