text: # 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 `radon_contextual_effects_minnesota_halfnormal`.
  
  Automatically generated using the command:
  
  ```
  python -m inference_gym.tools.get_ground_truth \
    --target=radon_contextual_effects_minnesota_halfnormal \
    --stan_samples=20000
  ```
  """
  
  import numpy as np
  
  IDENTITY_COUNTY_EFFECT_MEAN = np.array([
      1.369591126925,
      1.429199546375,
      1.387002967585,
      1.464424104745,
      1.3974416816500002,
      1.3760693444499998,
      1.51363212724,
      1.41885790623,
      1.3402308334900002,
      1.37411700389,
      1.4621094566000001,
      1.410747915025,
      1.4226956992950002,
      1.47028447685,
      1.3634621298199998,
      1.3654561768250002,
      1.3038799382700001,
      1.4150809289799997,
      1.350664381425,
      1.4148151482300002,
      1.437708842805,
      1.2019083646550002,
      1.356374356845,
      1.5058820788599998,
      1.51080020563,
      1.38755386455,
      1.37171472286,
      1.37499791654,
      1.411827915445,
      1.392432326615,
      1.45719288519,
      1.3765370694949999,
      1.47067556081,
      1.3855953171400004,
      1.355155763525,
      1.4759895069450002,
      1.256775057505,
      1.4930117613300002,
      1.414286980255,
      1.4600764939800002,
      1.44745894109,
      1.386269447735,
      1.3860683278450003,
      1.2827523040200002,
      1.29025026929,
      1.3607063844299998,
      1.351195199725,
      1.3366285332049999,
      1.4115117323699997,
      1.423993268425,
      1.471328163045,
      1.4178687145200002,
      1.34359736087,
      1.2559330168,
      1.4421920540950004,
      1.33928505256,
      1.284740506565,
      1.395357467045,
      1.3836275546849996,
      1.36931979686,
      1.4043974759449998,
      1.43225554231,
      1.379252530845,
      1.47163324763,
      1.3579540837250001,
      1.45561614521,
      1.4678627758349998,
      1.4327426859399999,
      1.34946241924,
      1.2065172112049998,
      1.36951253099,
      1.39121423402,
      1.396972311735,
      1.3080390071749999,
      1.4161848091749998,
      1.4139226526799997,
      1.44733993494,
      1.4251137335550002,
      1.2551259167775,
      1.3900362881050001,
      1.48802613494,
      1.41800448764,
      1.3313591477149997,
      1.481399039725,
      1.35811628464,
  ]).reshape((85,))
  
  IDENTITY_COUNTY_EFFECT_MEAN_STANDARD_ERROR = np.array([
      0.00044240066247013015,
      0.0003623274697258587,
      0.000467446120329379,
      0.0007272022444301067,
      0.0004581827149541065,
      0.0004129170165279406,
      0.0009048369761546262,
      0.0005179637813137002,
      0.0004557929050736222,
      0.00045428450215763346,
      0.0006550572100500181,
      0.00045149222240013976,
      0.000467875479779376,
      0.0006885045959126242,
      0.0004608531305874518,
      0.0004370548598470599,
      0.0006612046966424345,
      0.00046805298345567264,
      0.0002697539339415021,
      0.0004641904456273168,
      0.0005175916467929757,
      0.0011820501484208937,
      0.00047489011931019167,
      0.0009247454962754685,
      0.0008665838194788514,
      0.0002520442486474774,
      0.0004185701675260637,
      0.0004713759618087432,
      0.00046636965984123374,
      0.00038648197560482283,
      0.0006434097485803362,
      0.00040017676641425717,
      0.0007303848127140208,
      0.0004704007980175742,
      0.0004908021548308623,
      0.0008193114868283566,
      0.0008424020862045698,
      0.0009297130179617741,
      0.00048096381647912324,
      0.0006858021224709341,
      0.0005778597267097859,
      0.00044018527258034973,
      0.00047865119782658375,
      0.0007054356624224846,
      0.0006425394937348249,
      0.00041958232854139157,
      0.0004913111984993186,
      0.0004560347291189356,
      0.0004230061376168552,
      0.0005500479506494198,
      0.00075339443557322,
      0.0004944875332201594,
      0.0004941360707208552,
      0.0006771582554737279,
      0.0005784181903814173,
      0.0005222416510205879,
      0.0007243602764629056,
      0.00043350800325440015,
      0.0004554218257632572,
      0.00043128590550203956,
      0.000344689391797511,
      0.0005442605214331624,
      0.00044283192451517685,
      0.0006827524826279923,
      0.0004364359919876632,
      0.0006726398445608166,
      0.000678035371378916,
      0.0005023213429467947,
      0.0004369033990661497,
      0.0006383706061086843,
      0.0003334986094125572,
      0.0003519146504295781,
      0.00043105505383995394,
      0.0006148818038413253,
      0.0004958234600375501,
      0.00047828313156988387,
      0.0005891561866667608,
      0.0005406949975536809,
      0.0009385011303083106,
      0.0002991717560327451,
      0.000901391189198707,
      0.0005157552871820832,
      0.0004389628348820291,
      0.0007119729590609378,
      0.0004385505782063502,
  ]).reshape((85,))
  
  IDENTITY_COUNTY_EFFECT_STANDARD_DEVIATION = np.array([
      0.1478221843002874,
      0.10119131090461964,
      0.15166827156328622,
      0.14999917796590062,
      0.1469036762816015,
      0.1479629653670923,
      0.13613104365147385,
      0.14962958091722445,
      0.13223929497962017,
      0.1444559125774678,
      0.14832575566366984,
      0.14560513134306804,
      0.1413283938303161,
      0.13256863489043705,
      0.14882277310465142,
      0.15238181926617803,
      0.15657379698852394,
      0.13137286324614816,
      0.08117987120388688,
      0.14910319736766323,
      0.1350690330515568,
      0.1684177800860944,
      0.15469902778895658,
      0.14689280500185292,
      0.13481816467572663,
      0.06906307711775014,
      0.1424389313198612,
      0.14764254947203626,
      0.14947907352715126,
      0.12840058178492747,
      0.14693896261749034,
      0.14459113564144221,
      0.15282594085781506,
      0.15188397889894795,
      0.1453790121759248,
      0.16321280401477306,
      0.14672951797778266,
      0.16075861428008334,
      0.14410155539982267,
      0.15308082595524136,
      0.1386556287038865,
      0.1551404486915699,
      0.13948061083026173,
      0.14621291930501368,
      0.133966356891899,
      0.14206611095993035,
      0.15515576648558718,
      0.1341228098380888,
      0.12545448023337066,
      0.15789188555571348,
      0.15270957004450708,
      0.14892962958199046,
      0.1511936051638361,
      0.11975856869456396,
      0.14167834637232682,
      0.15405353209290235,
      0.14954149168311454,
      0.14649236633573356,
      0.14818726792313022,
      0.15213331485339845,
      0.10366173401021624,
      0.14583666871938458,
      0.1499594508273719,
      0.13378565654427627,
      0.15277602490768896,
      0.13824720662353424,
      0.1336735249542027,
      0.1365581654013424,
      0.14571253126152844,
      0.08478333126632623,
      0.10908421282596976,
      0.12849857581523155,
      0.15216235022577956,
      0.15053582815003444,
      0.1506792706384039,
      0.1474025662523754,
      0.14181296817025896,
      0.1475261872172126,
      0.16207129776953974,
      0.09173002567191572,
      0.16402524287579712,
      0.1580716641604231,
      0.12830283532491119,
      0.13139781002069048,
      0.15285553238767274,
  ]).reshape((85,))
  
  IDENTITY_COUNTY_EFFECT_MEAN_MEAN = np.array([
      1.392279422,
  ]).reshape(())
  
  IDENTITY_COUNTY_EFFECT_MEAN_MEAN_STANDARD_ERROR = np.array([
      0.00036505133240959185,
  ]).reshape(())
  
  IDENTITY_COUNTY_EFFECT_MEAN_STANDARD_DEVIATION = np.array([
      0.050712718961724836,
  ]).reshape(())
  
  IDENTITY_COUNTY_EFFECT_SCALE_MEAN = np.array([
      0.14457215533950002,
  ]).reshape(())
  
  IDENTITY_COUNTY_EFFECT_SCALE_MEAN_STANDARD_ERROR = np.array([
      0.0006768983639383711,
  ]).reshape(())
  
  IDENTITY_COUNTY_EFFECT_SCALE_STANDARD_DEVIATION = np.array([
      0.04918907312743993,
  ]).reshape(())
  
  IDENTITY_LOG_RADON_SCALE_MEAN = np.array([
      0.75977590157,
  ]).reshape(())
  
  IDENTITY_LOG_RADON_SCALE_MEAN_STANDARD_ERROR = np.array([
      7.327031648756984e-05,
  ]).reshape(())
  
  IDENTITY_LOG_RADON_SCALE_STANDARD_DEVIATION = np.array([
      0.018408179814628438,
  ]).reshape(())
  
  IDENTITY_WEIGHT_MEAN = np.array([
      0.7140640166199999,
      -0.7142173634200001,
      0.39877565798705394,
  ]).reshape((3,))
  
  IDENTITY_WEIGHT_MEAN_STANDARD_ERROR = np.array([
      0.00032238092978804445,
      0.0002013676034338986,
      0.0012256419602518147,
  ]).reshape((3,))
  
  IDENTITY_WEIGHT_STANDARD_DEVIATION = np.array([
      0.09035090550779404,
      0.07317535408346594,
      0.20066948570485357,
  ]).reshape((3,))
  