/test-data/lps_arrhythmia_log.txt

https://bitbucket.org/cistrome/cistrome-harvard/ · Plain Text · 197 lines · 174 code · 23 blank · 0 comment · 0 complexity · 0e5a82c4cc387eac4273c61d741c25de MD5 · raw file 

    

  1. Data set has 452 vectors with 279 features.
    2. 

  2.  Sampled 452 points out of 452
    3. 

  3.  calculateLambdaMax: n=279, m=452, m+=245, m-=207
    4. 

  4.  computed value of lambda_max: 1.8231e+02
    5. 

  5. 

  6.  **** Initial point: nz=0, f=   0.69314718056, lambda= 1.641e+02
    7. 

  7. iter    1, gpnorm=4.2035e-02, nonzero=   1 (  0.4%), function=6.931471805599e-01, alpha=1.0000e+00
    8. 

  8. iter    2, gpnorm=1.9781e-02, nonzero=   1 (  0.4%), function=6.903943411116e-01, alpha=8.0000e-01
    9. 

  9. iter    3, gpnorm=6.0325e-03, nonzero=   1 (  0.4%), function=6.896822613633e-01, alpha=6.4000e-01
    10. 

  10. iter    4, gpnorm=7.2193e-04, nonzero=   1 (  0.4%), function=6.896101060279e-01, alpha=5.1200e-01
    11. 

  11. iter    5, gpnorm=1.0530e-05, nonzero=   1 (  0.4%), function=6.896090566277e-01, alpha=4.0960e-01
    12. 

  12. iter    6, gpnorm=1.7618e-09, nonzero=   1 (  0.4%), function=6.896090564044e-01, alpha=3.2768e-01
    13. 

  13.  Function evals = 12,  Gradient evals =        6.0
    14. 

  14. 

  15.  **** Initial point: nz=1, f=  0.689609056404, lambda= 1.168e+02
    16. 

  16. iter    1, gpnorm=1.7618e-09, nonzero=   1 (  0.4%), function=6.896090564044e-01, alpha=3.2768e-01
    17. 

  17.  Function evals = 2,  Gradient evals =        1.0
    18. 

  18. 

  19.  **** Initial point: nz=1, f=  0.689609056404, lambda= 8.310e+01
    20. 

  20. iter    1, gpnorm=1.7618e-09, nonzero=   1 (  0.4%), function=6.896090564044e-01, alpha=3.2768e-01
    21. 

  21.  Function evals = 2,  Gradient evals =        1.0
    22. 

  22. 

  23.  **** Initial point: nz=1, f=  0.689609056404, lambda= 5.914e+01
    24. 

  24. iter    1, gpnorm=1.7618e-09, nonzero=   1 (  0.4%), function=6.896090564044e-01, alpha=3.2768e-01
    25. 

  25.  Function evals = 2,  Gradient evals =        1.0
    26. 

  26. 

  27.  **** Initial point: nz=1, f=  0.689609056404, lambda= 4.209e+01
    28. 

  28. iter    1, gpnorm=1.7618e-09, nonzero=   1 (  0.4%), function=6.896090564044e-01, alpha=3.2768e-01
    29. 

  29.  Function evals = 2,  Gradient evals =        1.0
    30. 

  30. 

  31.  **** Initial point: nz=1, f=  0.689609056404, lambda= 2.996e+01
    32. 

  32. iter    1, gpnorm=1.7618e-09, nonzero=   1 (  0.4%), function=6.896090564044e-01, alpha=3.2768e-01
    33. 

  33.  Function evals = 2,  Gradient evals =        1.0
    34. 

  34. 

  35.  **** Initial point: nz=1, f=  0.689609056404, lambda= 2.132e+01
    36. 

  36. iter    1, gpnorm=1.7618e-09, nonzero=   1 (  0.4%), function=6.896090564044e-01, alpha=3.2768e-01
    37. 

  37.  Function evals = 2,  Gradient evals =        1.0
    38. 

  38. 

  39.  **** Initial point: nz=1, f=  0.689609056404, lambda= 1.517e+01
    40. 

  40. iter    1, gpnorm=1.7618e-09, nonzero=   1 (  0.4%), function=6.896090564044e-01, alpha=3.2768e-01
    41. 

  41.  Function evals = 2,  Gradient evals =        1.0
    42. 

  42. 

  43.  **** Initial point: nz=1, f=  0.689609056404, lambda= 1.080e+01
    44. 

  44. iter    1, gpnorm=1.7618e-09, nonzero=   1 (  0.4%), function=6.896090564044e-01, alpha=3.2768e-01
    45. 

  45.  Function evals = 2,  Gradient evals =        1.0
    46. 

  46. 

  47.  **** Initial point: nz=1, f=  0.689609056404, lambda= 7.685e+00
    48. 

  48. iter    1, gpnorm=1.7618e-09, nonzero=   1 (  0.4%), function=6.896090564044e-01, alpha=3.2768e-01
    49. 

  49.  Function evals = 2,  Gradient evals =        1.0
    50. 

  50. 

  51.  **** Initial point: nz=1, f=  0.689609056404, lambda= 5.469e+00
    52. 

  52. iter    1, gpnorm=1.7618e-09, nonzero=   1 (  0.4%), function=6.896090564044e-01, alpha=3.2768e-01
    53. 

  53.  Function evals = 2,  Gradient evals =        1.0
    54. 

  54. 

  55. lambda=1.64e+02 solution:
    56. 

  56.   optimal log-likelihood function value: 6.90e-01
    57. 

  57.   optimal *regularized* log-likelihood function value: 6.90e-01
    58. 

  58.   number of non-zeros at the optimum:      1
    59. 

  59.   number of iterations required:      6
    60. 

  60.   prediction using this solution:
    61. 

  61.     54.20% of vectors were correctly predicted.
    62. 

  62.     245 correctly predicted.
    63. 

  63.     207 in +1 predicted to be in -1.
    64. 

  64.     0 in -1 predicted to be in +1.
    65. 

  65.     0 in +1 with 50/50 chance.
    66. 

  66.     0 in -1 with 50/50 chance.
    67. 

  67. 

  68. lambda=1.17e+02 solution:
    69. 

  69.   optimal log-likelihood function value: 6.90e-01
    70. 

  70.   optimal *regularized* log-likelihood function value: 6.90e-01
    71. 

  71.   number of non-zeros at the optimum:      1
    72. 

  72.   number of iterations required:      1
    73. 

  73.   prediction using this solution:
    74. 

  74.     54.20% of vectors were correctly predicted.
    75. 

  75.     245 correctly predicted.
    76. 

  76.     207 in +1 predicted to be in -1.
    77. 

  77.     0 in -1 predicted to be in +1.
    78. 

  78.     0 in +1 with 50/50 chance.
    79. 

  79.     0 in -1 with 50/50 chance.
    80. 

  80. 

  81. lambda=8.31e+01 solution:
    82. 

  82.   optimal log-likelihood function value: 6.90e-01
    83. 

  83.   optimal *regularized* log-likelihood function value: 6.90e-01
    84. 

  84.   number of non-zeros at the optimum:      1
    85. 

  85.   number of iterations required:      1
    86. 

  86.   prediction using this solution:
    87. 

  87.     54.20% of vectors were correctly predicted.
    88. 

  88.     245 correctly predicted.
    89. 

  89.     207 in +1 predicted to be in -1.
    90. 

  90.     0 in -1 predicted to be in +1.
    91. 

  91.     0 in +1 with 50/50 chance.
    92. 

  92.     0 in -1 with 50/50 chance.
    93. 

  93. 

  94. lambda=5.91e+01 solution:
    95. 

  95.   optimal log-likelihood function value: 6.90e-01
    96. 

  96.   optimal *regularized* log-likelihood function value: 6.90e-01
    97. 

  97.   number of non-zeros at the optimum:      1
    98. 

  98.   number of iterations required:      1
    99. 

  99.   prediction using this solution:
    100. 

  100.     54.20% of vectors were correctly predicted.
    101. 

  101.     245 correctly predicted.
    102. 

  102.     207 in +1 predicted to be in -1.
    103. 

  103.     0 in -1 predicted to be in +1.
    104. 

  104.     0 in +1 with 50/50 chance.
    105. 

  105.     0 in -1 with 50/50 chance.
    106. 

  106. 

  107. lambda=4.21e+01 solution:
    108. 

  108.   optimal log-likelihood function value: 6.90e-01
    109. 

  109.   optimal *regularized* log-likelihood function value: 6.90e-01
    110. 

  110.   number of non-zeros at the optimum:      1
    111. 

  111.   number of iterations required:      1
    112. 

  112.   prediction using this solution:
    113. 

  113.     54.20% of vectors were correctly predicted.
    114. 

  114.     245 correctly predicted.
    115. 

  115.     207 in +1 predicted to be in -1.
    116. 

  116.     0 in -1 predicted to be in +1.
    117. 

  117.     0 in +1 with 50/50 chance.
    118. 

  118.     0 in -1 with 50/50 chance.
    119. 

  119. 

  120. lambda=3.00e+01 solution:
    121. 

  121.   optimal log-likelihood function value: 6.90e-01
    122. 

  122.   optimal *regularized* log-likelihood function value: 6.90e-01
    123. 

  123.   number of non-zeros at the optimum:      1
    124. 

  124.   number of iterations required:      1
    125. 

  125.   prediction using this solution:
    126. 

  126.     54.20% of vectors were correctly predicted.
    127. 

  127.     245 correctly predicted.
    128. 

  128.     207 in +1 predicted to be in -1.
    129. 

  129.     0 in -1 predicted to be in +1.
    130. 

  130.     0 in +1 with 50/50 chance.
    131. 

  131.     0 in -1 with 50/50 chance.
    132. 

  132. 

  133. lambda=2.13e+01 solution:
    134. 

  134.   optimal log-likelihood function value: 6.90e-01
    135. 

  135.   optimal *regularized* log-likelihood function value: 6.90e-01
    136. 

  136.   number of non-zeros at the optimum:      1
    137. 

  137.   number of iterations required:      1
    138. 

  138.   prediction using this solution:
    139. 

  139.     54.20% of vectors were correctly predicted.
    140. 

  140.     245 correctly predicted.
    141. 

  141.     207 in +1 predicted to be in -1.
    142. 

  142.     0 in -1 predicted to be in +1.
    143. 

  143.     0 in +1 with 50/50 chance.
    144. 

  144.     0 in -1 with 50/50 chance.
    145. 

  145. 

  146. lambda=1.52e+01 solution:
    147. 

  147.   optimal log-likelihood function value: 6.90e-01
    148. 

  148.   optimal *regularized* log-likelihood function value: 6.90e-01
    149. 

  149.   number of non-zeros at the optimum:      1
    150. 

  150.   number of iterations required:      1
    151. 

  151.   prediction using this solution:
    152. 

  152.     54.20% of vectors were correctly predicted.
    153. 

  153.     245 correctly predicted.
    154. 

  154.     207 in +1 predicted to be in -1.
    155. 

  155.     0 in -1 predicted to be in +1.
    156. 

  156.     0 in +1 with 50/50 chance.
    157. 

  157.     0 in -1 with 50/50 chance.
    158. 

  158. 

  159. lambda=1.08e+01 solution:
    160. 

  160.   optimal log-likelihood function value: 6.90e-01
    161. 

  161.   optimal *regularized* log-likelihood function value: 6.90e-01
    162. 

  162.   number of non-zeros at the optimum:      1
    163. 

  163.   number of iterations required:      1
    164. 

  164.   prediction using this solution:
    165. 

  165.     54.20% of vectors were correctly predicted.
    166. 

  166.     245 correctly predicted.
    167. 

  167.     207 in +1 predicted to be in -1.
    168. 

  168.     0 in -1 predicted to be in +1.
    169. 

  169.     0 in +1 with 50/50 chance.
    170. 

  170.     0 in -1 with 50/50 chance.
    171. 

  171. 

  172. lambda=7.68e+00 solution:
    173. 

  173.   optimal log-likelihood function value: 6.90e-01
    174. 

  174.   optimal *regularized* log-likelihood function value: 6.90e-01
    175. 

  175.   number of non-zeros at the optimum:      1
    176. 

  176.   number of iterations required:      1
    177. 

  177.   prediction using this solution:
    178. 

  178.     54.20% of vectors were correctly predicted.
    179. 

  179.     245 correctly predicted.
    180. 

  180.     207 in +1 predicted to be in -1.
    181. 

  181.     0 in -1 predicted to be in +1.
    182. 

  182.     0 in +1 with 50/50 chance.
    183. 

  183.     0 in -1 with 50/50 chance.
    184. 

  184. 

  185. lambda=5.47e+00 solution:
    186. 

  186.   optimal log-likelihood function value: 6.90e-01
    187. 

  187.   optimal *regularized* log-likelihood function value: 6.90e-01
    188. 

  188.   number of non-zeros at the optimum:      1
    189. 

  189.   number of iterations required:      1
    190. 

  190.   prediction using this solution:
    191. 

  191.     54.20% of vectors were correctly predicted.
    192. 

  192.     245 correctly predicted.
    193. 

  193.     207 in +1 predicted to be in -1.
    194. 

  194.     0 in -1 predicted to be in +1.
    195. 

  195.     0 in +1 with 50/50 chance.
    196. 

  196.     0 in -1 with 50/50 chance.
    197.