text: # Lint as: python3
  # Copyright 2020 The TensorFlow Probability Authors.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ============================================================================
  r"""Ground truth values for `stochastic_volatility_log_sp500_small`.
  
  Automatically generated using the command:
  
  ```
  python -m inference_gym.tools.get_ground_truth \
    --target=stochastic_volatility_log_sp500_small \
    --stan_samples=50000
  ```
  """
  
  import numpy as np
  
  IDENTITY_LOG_VOLATILITY_MEAN = np.array([
      -8.8733405736,
      -8.881073967399999,
      -9.073473969779998,
      -9.354620309400001,
      -9.509948941980003,
      -9.595035254439999,
      -9.73535877716,
      -9.74587096516,
      -9.82309774416,
      -9.74651971124,
      -9.55477168132,
      -9.238956012100001,
      -8.850009807300001,
      -8.32491795628,
      -7.7298244822600015,
      -7.448367501140001,
      -7.306349496899999,
      -6.98725180674,
      -6.907023489560001,
      -6.666585622400001,
      -6.563983841859999,
      -6.400931128900001,
      -6.2775754702,
      -6.1017447477,
      -5.781700613980001,
      -5.6535959051799995,
      -5.473931157420001,
      -5.232433479559999,
      -5.14161940846,
      -5.122223355920001,
      -5.39258476024,
      -5.62792970564,
      -5.846401729119999,
      -5.888795914399999,
      -5.89785786174,
      -5.8020158783,
      -6.0270740812,
      -6.08606140272,
      -6.28386214948,
      -6.4426856254799985,
      -6.5681649083,
      -6.5581909178,
      -6.6583122976,
      -6.6750123100400005,
      -6.54944072238,
      -6.8616020138,
      -6.996565951299999,
      -7.220386458920001,
      -7.34495693864,
      -7.32777109334,
      -7.42924225168,
      -7.5360513713,
      -7.49230458128,
      -7.53890811512,
      -7.531851298180001,
      -7.69251793462,
      -7.9274546296,
      -7.9932550016,
      -8.03200927292,
      -8.05461413504,
      -7.9353561309,
      -8.02957664072,
      -8.015215484540002,
      -8.28230805492,
      -8.40673853508,
      -8.464030835159999,
      -8.40773389506,
      -8.333687898880001,
      -8.34935625724,
      -8.20625900992,
      -8.19486739086,
      -8.221362148879999,
      -8.194979681880001,
      -8.026834013980002,
      -8.2463375823,
      -8.38507813002,
      -8.611809648080001,
      -8.74851442642,
      -8.73147698514,
      -8.7719762211,
      -8.966727014140002,
      -9.00658548024,
      -8.952497331660002,
      -8.77647424642,
      -8.55436472924,
      -8.396726254879999,
      -8.08681594808,
      -8.045787058139998,
      -7.94257429286,
      -7.71243166352,
      -7.321332104600001,
      -7.704935289939999,
      -8.0117780516,
      -8.201209157280001,
      -8.517860974600001,
      -8.69022124292,
      -8.70680058278,
      -8.614365744259997,
      -8.433266848479999,
      -8.11843029122,
  ]).reshape((100,))
  
  IDENTITY_LOG_VOLATILITY_MEAN_STANDARD_ERROR = np.array([
      0.001552323187494642,
      0.0013926324263184714,
      0.0013103013199364843,
      0.0014375165864973542,
      0.0015032445388355878,
      0.001549643788229025,
      0.0016556075721341707,
      0.0017363103541468152,
      0.0019938501149814043,
      0.0020926894138799017,
      0.0019974508228620294,
      0.0018524431353649888,
      0.001724018782327666,
      0.001429900874797678,
      0.0011040838661172765,
      0.0011596588406403855,
      0.0013474554900043249,
      0.0011045834632282506,
      0.0013018788738102971,
      0.0012863377390118114,
      0.001293227647982947,
      0.0011966381329006335,
      0.0011575643283686088,
      0.001258684893869969,
      0.0010811160485445725,
      0.001111898278343125,
      0.0012448710356213794,
      0.0011717496955548857,
      0.0011938406945359405,
      0.0011982817802643874,
      0.0012077045251251276,
      0.0012824231929925497,
      0.00142905898104745,
      0.0013115180108953667,
      0.0013293653320059886,
      0.0012212115741200178,
      0.0012907312129513207,
      0.0012277611212555,
      0.0013008659254223666,
      0.0013854692884899618,
      0.0012942386689868243,
      0.0012014324244202562,
      0.0012350859061246563,
      0.0012786856834256755,
      0.0012156154446916903,
      0.0012302007281491329,
      0.0012325170907999201,
      0.0013688744124849344,
      0.0014207970511611852,
      0.001324098065816587,
      0.0012664019938218893,
      0.001397194488672574,
      0.0012506106714636576,
      0.0011265094955663951,
      0.0012433042236626242,
      0.0012513308983475011,
      0.0013664451299075736,
      0.0013732144191596508,
      0.0012610347230284395,
      0.0013172086186877391,
      0.0011477507333033897,
      0.0012145330705824497,
      0.0010933577366119217,
      0.001447138585827099,
      0.0014462439866045722,
      0.0014664191620388867,
      0.0012637445988015585,
      0.0011624193203776858,
      0.00129848531108935,
      0.0012718595429627843,
      0.0013015055724683602,
      0.0012717225246978228,
      0.0012544326274886344,
      0.0011918076457523594,
      0.0011424921558513181,
      0.0012363485030006851,
      0.0013933936187340496,
      0.0015976832589076413,
      0.0012717816770836253,
      0.0013492639093347584,
      0.0014804333859710977,
      0.0016019027524811812,
      0.0015913036634505407,
      0.0013942358140949542,
      0.0014799669462635036,
      0.0013736117007746122,
      0.0010903500624916632,
      0.0012012976480960697,
      0.0012503187070283738,
      0.001226302193255055,
      0.0010536656553271672,
      0.0011947483894009182,
      0.0013050246019980418,
      0.0013757271290735801,
      0.0017186975920047193,
      0.001850835451914996,
      0.001794935487647617,
      0.0016806019512671988,
      0.001591795639938317,
      0.0013932623115271822,
  ]).reshape((100,))
  
  IDENTITY_LOG_VOLATILITY_STANDARD_DEVIATION = np.array([
      0.7883942896908247,
      0.6707581592155326,
      0.6594290397639717,
      0.7238888422458728,
      0.7346093870816314,
      0.7217407907311835,
      0.7872017985954664,
      0.7829188855025379,
      0.8867309014501862,
      0.9107486333515284,
      0.8962203375120152,
      0.8354559501286077,
      0.7838570707138139,
      0.6724124584106372,
      0.561782315739672,
      0.5779560561642701,
      0.6334883130663533,
      0.5825038017583866,
      0.6405883623903733,
      0.6003563898282948,
      0.6298466665240854,
      0.6236791523414743,
      0.6449389696355493,
      0.6573981637306017,
      0.5914147623705175,
      0.6186554021275875,
      0.6188758199291787,
      0.5796985540204287,
      0.5780506170556018,
      0.5583106922650058,
      0.6101313698604983,
      0.6460804213490317,
      0.7000004452448387,
      0.6635755296954704,
      0.6450826315859404,
      0.5708556186477389,
      0.6346394254558391,
      0.5996884193796639,
      0.6359232098155769,
      0.6502732159920808,
      0.668973579234075,
      0.6212771177246842,
      0.6497172483741462,
      0.6397428817875402,
      0.5550184511205836,
      0.6406273693384656,
      0.6217666068980068,
      0.6712872082075425,
      0.6810379013254083,
      0.622340063616422,
      0.6432641439374791,
      0.6701279103398072,
      0.6180376320941331,
      0.6219492101881677,
      0.5917884528455905,
      0.617973938976343,
      0.6874021474865369,
      0.6713837408640286,
      0.6601525839298861,
      0.6659859905855088,
      0.5928942608040771,
      0.6265788602911617,
      0.5821938827272539,
      0.6776094016433267,
      0.6950481960873309,
      0.7034007778844767,
      0.6635883075799696,
      0.6317154085284811,
      0.6682741407810169,
      0.6095565228153066,
      0.6178067206068949,
      0.6415694297537321,
      0.6422995989238723,
      0.5615719591288677,
      0.61824293746151,
      0.6205966603438415,
      0.681366923419449,
      0.7047133371683475,
      0.6459813513537295,
      0.6376589611296286,
      0.7382127118037395,
      0.759472279643925,
      0.7599750361030426,
      0.7039731615842157,
      0.6570870015917741,
      0.6672447553017251,
      0.5858206814551213,
      0.6399584112711255,
      0.6671022086714598,
      0.6300469966548496,
      0.5144180990799809,
      0.6086587669106681,
      0.6596633112360383,
      0.655748983706467,
      0.7812329396104841,
      0.8473014355563592,
      0.8348199860853812,
      0.8013984849109356,
      0.7668500992613106,
      0.7003484652379587,
  ]).reshape((100,))
  
  IDENTITY_MEAN_LOG_VOLATILITY_MEAN = np.array([
      -7.661182867498001,
  ]).reshape(())
  
  IDENTITY_MEAN_LOG_VOLATILITY_MEAN_STANDARD_ERROR = np.array([
      0.006203773273164915,
  ]).reshape(())
  
  IDENTITY_MEAN_LOG_VOLATILITY_STANDARD_DEVIATION = np.array([
      1.1869502068074804,
  ]).reshape(())
  
  IDENTITY_PERSISTENCE_OF_VOLATILITY_MEAN = np.array([
      0.9316393021399998,
  ]).reshape(())
  
  IDENTITY_PERSISTENCE_OF_VOLATILITY_MEAN_STANDARD_ERROR = np.array([
      0.00014480807389693925,
  ]).reshape(())
  
  IDENTITY_PERSISTENCE_OF_VOLATILITY_STANDARD_DEVIATION = np.array([
      0.042975142142105516,
  ]).reshape(())
  
  IDENTITY_WHITE_NOISE_SHOCK_SCALE_MEAN = np.array([
      0.533825742426,
  ]).reshape(())
  
  IDENTITY_WHITE_NOISE_SHOCK_SCALE_MEAN_STANDARD_ERROR = np.array([
      0.0003975251462025709,
  ]).reshape(())
  
  IDENTITY_WHITE_NOISE_SHOCK_SCALE_STANDARD_DEVIATION = np.array([
      0.14040332147525458,
  ]).reshape(())
  