ML之分类预测之ElasticNet之PLoR：在二分类数据集上调用Glmnet库训练PLoR模型(T2)

ML之分类预测之ElasticNet之PLoR：在二分类数据集上调用Glmnet库训练PLoR模型(T2)

目录

输出结果

设计思路

核心代码

输出结果

设计思路

核心代码

      for iStep in range(nSteps):
       lam = lam * lamMult
       betaIRLS = list(beta)
       beta0IRLS = beta0
       distIRLS = 100.0
       iterIRLS = 0
      while distIRLS > 0.01:
       iterIRLS += 1
       iterInner = 0.0
       betaInner = list(betaIRLS)
       beta0Inner = beta0IRLS
       distInner = 100.0
      while distInner > 0.01:
       iterInner += 1
      if iterInner > 100: break
       betaStart = list(betaInner)
      for iCol in range(ncol):
       sumWxr = 0.0
       sumWxx = 0.0
       sumWr = 0.0
       sumW = 0.0
      for iRow in range(nrow):
       x = list(xNormalized[iRow])
       y = labels[iRow]
       p = Pr(beta0IRLS, betaIRLS, x)
      if abs(p) < 1e-5:
       p = 0.0
       w = 1e-5
      elif abs(1.0 - p) < 1e-5:
       p = 1.0
       w = 1e-5
      else:
       w = p * (1.0 - p)
       z = (y - p) / w + beta0IRLS + sum([x[i] * betaIRLS[i] for i in range(ncol)])
       r = z - beta0Inner - sum([x[i] * betaInner[i] for i in range(ncol)])
       sumWxr += w * x[iCol] * r
       sumWxx += w * x[iCol] * x[iCol]
       sumWr += w * r
       sumW += w
       avgWxr = sumWxr / nrow
       avgWxx = sumWxx / nrow
       beta0Inner = beta0Inner + sumWr / sumW
       uncBeta = avgWxr + avgWxx * betaInner[iCol]
       betaInner[iCol] = S(uncBeta, lam * alpha) / (avgWxx + lam * (1.0 - alpha))
       sumDiff = sum([abs(betaInner[n] - betaStart[n]) for n in range(ncol)])
       sumBeta = sum([abs(betaInner[n]) for n in range(ncol)])
       distInner = sumDiff/sumBeta
       a = sum([abs(betaIRLS[i] - betaInner[i]) for i in range(ncol)])
       b = sum([abs(betaIRLS[i]) for i in range(ncol)])
       distIRLS = a / (b + 0.0001)
       dBeta = [betaInner[i] - betaIRLS[i] for i in range(ncol)]
       gradStep = 1.0
       temp = [betaIRLS[i] + gradStep * dBeta[i] for i in range(ncol)]
       betaIRLS = list(temp)
       beta = list(betaIRLS)
       beta0 = beta0IRLS
       betaMat.append(list(beta))
       beta0List.append(beta0)
       nzBeta = [index for index in range(ncol) if beta[index] != 0.0]
      for q in nzBeta:
      if not(q in nzList):
       nzList.append(q)
  
 

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原文链接：yunyaniu.blog.csdn.net/article/details/85857823