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1 Data set has 452 vectors with 279 features.
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2 Sampled 452 points out of 452
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3 calculateLambdaMax: n=279, m=452, m+=245, m-=207
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4 computed value of lambda_max: 1.8231e+02
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5
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6 **** Initial point: nz=0, f= 0.69314718056, lambda= 1.641e+02
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7 iter 1, gpnorm=4.2035e-02, nonzero= 1 ( 0.4%), function=6.931471805599e-01, alpha=1.0000e+00
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8 iter 2, gpnorm=1.9781e-02, nonzero= 1 ( 0.4%), function=6.903943411116e-01, alpha=8.0000e-01
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9 iter 3, gpnorm=6.0325e-03, nonzero= 1 ( 0.4%), function=6.896822613633e-01, alpha=6.4000e-01
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10 iter 4, gpnorm=7.2193e-04, nonzero= 1 ( 0.4%), function=6.896101060279e-01, alpha=5.1200e-01
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11 iter 5, gpnorm=1.0530e-05, nonzero= 1 ( 0.4%), function=6.896090566277e-01, alpha=4.0960e-01
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12 iter 6, gpnorm=1.7618e-09, nonzero= 1 ( 0.4%), function=6.896090564044e-01, alpha=3.2768e-01
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13 Function evals = 12, Gradient evals = 6.0
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14
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15 **** Initial point: nz=1, f= 0.689609056404, lambda= 1.168e+02
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16 iter 1, gpnorm=1.7618e-09, nonzero= 1 ( 0.4%), function=6.896090564044e-01, alpha=3.2768e-01
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17 Function evals = 2, Gradient evals = 1.0
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18
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19 **** Initial point: nz=1, f= 0.689609056404, lambda= 8.310e+01
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20 iter 1, gpnorm=1.7618e-09, nonzero= 1 ( 0.4%), function=6.896090564044e-01, alpha=3.2768e-01
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21 Function evals = 2, Gradient evals = 1.0
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22
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23 **** Initial point: nz=1, f= 0.689609056404, lambda= 5.914e+01
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24 iter 1, gpnorm=1.7618e-09, nonzero= 1 ( 0.4%), function=6.896090564044e-01, alpha=3.2768e-01
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25 Function evals = 2, Gradient evals = 1.0
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26
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27 **** Initial point: nz=1, f= 0.689609056404, lambda= 4.209e+01
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28 iter 1, gpnorm=1.7618e-09, nonzero= 1 ( 0.4%), function=6.896090564044e-01, alpha=3.2768e-01
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29 Function evals = 2, Gradient evals = 1.0
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30
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31 **** Initial point: nz=1, f= 0.689609056404, lambda= 2.996e+01
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32 iter 1, gpnorm=1.7618e-09, nonzero= 1 ( 0.4%), function=6.896090564044e-01, alpha=3.2768e-01
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33 Function evals = 2, Gradient evals = 1.0
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34
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35 **** Initial point: nz=1, f= 0.689609056404, lambda= 2.132e+01
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36 iter 1, gpnorm=1.7618e-09, nonzero= 1 ( 0.4%), function=6.896090564044e-01, alpha=3.2768e-01
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37 Function evals = 2, Gradient evals = 1.0
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38
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39 **** Initial point: nz=1, f= 0.689609056404, lambda= 1.517e+01
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40 iter 1, gpnorm=1.7618e-09, nonzero= 1 ( 0.4%), function=6.896090564044e-01, alpha=3.2768e-01
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41 Function evals = 2, Gradient evals = 1.0
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42
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43 **** Initial point: nz=1, f= 0.689609056404, lambda= 1.080e+01
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44 iter 1, gpnorm=1.7618e-09, nonzero= 1 ( 0.4%), function=6.896090564044e-01, alpha=3.2768e-01
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45 Function evals = 2, Gradient evals = 1.0
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46
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47 **** Initial point: nz=1, f= 0.689609056404, lambda= 7.685e+00
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48 iter 1, gpnorm=1.7618e-09, nonzero= 1 ( 0.4%), function=6.896090564044e-01, alpha=3.2768e-01
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49 Function evals = 2, Gradient evals = 1.0
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50
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51 **** Initial point: nz=1, f= 0.689609056404, lambda= 5.469e+00
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52 iter 1, gpnorm=1.7618e-09, nonzero= 1 ( 0.4%), function=6.896090564044e-01, alpha=3.2768e-01
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53 Function evals = 2, Gradient evals = 1.0
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54
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55 lambda=1.64e+02 solution:
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56 optimal log-likelihood function value: 6.90e-01
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57 optimal *regularized* log-likelihood function value: 6.90e-01
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58 number of non-zeros at the optimum: 1
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59 number of iterations required: 6
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60 prediction using this solution:
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61 54.20% of vectors were correctly predicted.
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62 245 correctly predicted.
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63 207 in +1 predicted to be in -1.
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64 0 in -1 predicted to be in +1.
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65 0 in +1 with 50/50 chance.
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66 0 in -1 with 50/50 chance.
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67
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68 lambda=1.17e+02 solution:
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69 optimal log-likelihood function value: 6.90e-01
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70 optimal *regularized* log-likelihood function value: 6.90e-01
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71 number of non-zeros at the optimum: 1
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72 number of iterations required: 1
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73 prediction using this solution:
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74 54.20% of vectors were correctly predicted.
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75 245 correctly predicted.
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76 207 in +1 predicted to be in -1.
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77 0 in -1 predicted to be in +1.
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78 0 in +1 with 50/50 chance.
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79 0 in -1 with 50/50 chance.
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80
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81 lambda=8.31e+01 solution:
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82 optimal log-likelihood function value: 6.90e-01
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83 optimal *regularized* log-likelihood function value: 6.90e-01
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84 number of non-zeros at the optimum: 1
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85 number of iterations required: 1
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86 prediction using this solution:
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87 54.20% of vectors were correctly predicted.
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88 245 correctly predicted.
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89 207 in +1 predicted to be in -1.
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90 0 in -1 predicted to be in +1.
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91 0 in +1 with 50/50 chance.
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92 0 in -1 with 50/50 chance.
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93
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94 lambda=5.91e+01 solution:
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95 optimal log-likelihood function value: 6.90e-01
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96 optimal *regularized* log-likelihood function value: 6.90e-01
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97 number of non-zeros at the optimum: 1
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98 number of iterations required: 1
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99 prediction using this solution:
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100 54.20% of vectors were correctly predicted.
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101 245 correctly predicted.
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102 207 in +1 predicted to be in -1.
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103 0 in -1 predicted to be in +1.
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104 0 in +1 with 50/50 chance.
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105 0 in -1 with 50/50 chance.
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106
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107 lambda=4.21e+01 solution:
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108 optimal log-likelihood function value: 6.90e-01
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109 optimal *regularized* log-likelihood function value: 6.90e-01
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110 number of non-zeros at the optimum: 1
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111 number of iterations required: 1
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112 prediction using this solution:
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113 54.20% of vectors were correctly predicted.
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114 245 correctly predicted.
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115 207 in +1 predicted to be in -1.
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116 0 in -1 predicted to be in +1.
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117 0 in +1 with 50/50 chance.
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118 0 in -1 with 50/50 chance.
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119
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120 lambda=3.00e+01 solution:
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121 optimal log-likelihood function value: 6.90e-01
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122 optimal *regularized* log-likelihood function value: 6.90e-01
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123 number of non-zeros at the optimum: 1
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124 number of iterations required: 1
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125 prediction using this solution:
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126 54.20% of vectors were correctly predicted.
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127 245 correctly predicted.
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128 207 in +1 predicted to be in -1.
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129 0 in -1 predicted to be in +1.
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130 0 in +1 with 50/50 chance.
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131 0 in -1 with 50/50 chance.
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132
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133 lambda=2.13e+01 solution:
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134 optimal log-likelihood function value: 6.90e-01
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135 optimal *regularized* log-likelihood function value: 6.90e-01
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136 number of non-zeros at the optimum: 1
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137 number of iterations required: 1
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138 prediction using this solution:
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139 54.20% of vectors were correctly predicted.
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140 245 correctly predicted.
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141 207 in +1 predicted to be in -1.
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142 0 in -1 predicted to be in +1.
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143 0 in +1 with 50/50 chance.
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144 0 in -1 with 50/50 chance.
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145
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146 lambda=1.52e+01 solution:
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147 optimal log-likelihood function value: 6.90e-01
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148 optimal *regularized* log-likelihood function value: 6.90e-01
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149 number of non-zeros at the optimum: 1
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150 number of iterations required: 1
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151 prediction using this solution:
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152 54.20% of vectors were correctly predicted.
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153 245 correctly predicted.
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154 207 in +1 predicted to be in -1.
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155 0 in -1 predicted to be in +1.
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156 0 in +1 with 50/50 chance.
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157 0 in -1 with 50/50 chance.
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158
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159 lambda=1.08e+01 solution:
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160 optimal log-likelihood function value: 6.90e-01
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161 optimal *regularized* log-likelihood function value: 6.90e-01
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162 number of non-zeros at the optimum: 1
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163 number of iterations required: 1
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164 prediction using this solution:
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165 54.20% of vectors were correctly predicted.
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166 245 correctly predicted.
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167 207 in +1 predicted to be in -1.
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168 0 in -1 predicted to be in +1.
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169 0 in +1 with 50/50 chance.
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170 0 in -1 with 50/50 chance.
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171
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172 lambda=7.68e+00 solution:
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173 optimal log-likelihood function value: 6.90e-01
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174 optimal *regularized* log-likelihood function value: 6.90e-01
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175 number of non-zeros at the optimum: 1
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176 number of iterations required: 1
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177 prediction using this solution:
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178 54.20% of vectors were correctly predicted.
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179 245 correctly predicted.
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180 207 in +1 predicted to be in -1.
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181 0 in -1 predicted to be in +1.
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182 0 in +1 with 50/50 chance.
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183 0 in -1 with 50/50 chance.
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184
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185 lambda=5.47e+00 solution:
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186 optimal log-likelihood function value: 6.90e-01
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187 optimal *regularized* log-likelihood function value: 6.90e-01
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188 number of non-zeros at the optimum: 1
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189 number of iterations required: 1
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190 prediction using this solution:
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191 54.20% of vectors were correctly predicted.
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192 245 correctly predicted.
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193 207 in +1 predicted to be in -1.
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194 0 in -1 predicted to be in +1.
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195 0 in +1 with 50/50 chance.
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196 0 in -1 with 50/50 chance.
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197
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