Package com.mockturtlesolutions.snifflib.datatypes

Examples of com.mockturtlesolutions.snifflib.datatypes.DblMatrix.pow()

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    concMatrix.show("concMatrix");
    this.BVector.show("BVector");
   
   
    DblMatrix ionicStrPenalty = iStrength.minus(this.desiredIonicStr);
    ionicStrPenalty = ionicStrPenalty.pow(2);
   
    DblMatrix resids = (this.AMatrix.dot(concMatrix)).minus(this.BVector);
    DblMatrix ONE = new DblMatrix(1.0);
   
    resids = resids.getMap(ONE.minus(DblMatrix.isNaN(resids)));
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              thisKaSum = (DblMatrix)OUT.get(param);
              thisKaSum = thisKaSum.plus(pka);
              OUT.put(param,thisKaSum);
             
              thisKaSumSq = (DblMatrix)SUMSQ.get(param);
              thisKaSumSq = thisKaSumSq.plus(pka.pow(2.0));
              SUMSQ.put(param,thisKaSumSq);
             
              int N = ((Integer)divisors.get(param)).intValue();
              divisors.put(param,new Integer(N+1));
            }
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              DblMatrix pka = DblMatrix.log10(Ka[i].getDblAt(k)).times(-1.0);
             
              thisKaSum = pka;
              OUT.put(param,thisKaSum);
             
              thisKaSumSq = pka.pow(2);
              SUMSQ.put(param,thisKaSumSq);
             
              divisors.put(param,new Integer(1));
            }
          }
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              thisKbSum = (DblMatrix)OUT.get(param);
              thisKbSum = thisKbSum.plus(pkb);
              OUT.put(param,thisKbSum);
             
              thisKbSumSq = (DblMatrix)SUMSQ.get(param);
              thisKbSumSq = thisKbSumSq.plus(pkb.pow(2.0));
              SUMSQ.put(param,thisKbSumSq);
             
              int N = ((Integer)divisors.get(param)).intValue();
              divisors.put(param,new Integer(N+1));
            }
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              DblMatrix pkb = DblMatrix.log10(Kb[i].getDblAt(k)).times(-1.0);
             
              thisKbSum = pkb;
              OUT.put(param,thisKbSum);
             
              thisKbSumSq = pkb.pow(2);
              SUMSQ.put(param,thisKbSumSq);
             
              divisors.put(param,new Integer(1));
            }
          }
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      DblParamSet pparms = this.getPriorParams();
      pparms.setParam(params[n],pKaSum.divideBy(((Integer)divisors.get(params[n])).intValue()));
     
      pKaSum = ((DblMatrix)SUMSQ.get(params[n])).minus((pKaSum.pow(2.0)).divideBy(((Integer)divisors.get(params[n])).intValue()));
      //this.stdcoefficients.setParam(params[n],pKaSum);
      pparms.setParam(params[n]+"_sigma",DblMatrix.sqrt(pKaSum));//Set the standard deviation in the priorParameters.
     
    }   
  }
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