사례기반추론 예제 - 2
간단한 예제를 R을 통하여 알아본다
데이터 설명
R분석
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분석
# 1) Old Case Read oCR = read.csv('old_data.csv', header = T , stringsAsFactors=F ) # 2) New Case read nCR = read.csv('new_data.csv', header = T , stringsAsFactors=F ) # 3) 가중치부여여 wg_list = list(wg_n1=0.05, wg_n2=0.05, wg_n3=0.15, wg_n4=0.15, wg_n5=0.20, wg_n6=0.15, wg_c1=0.10 , wg_c2=0.15) # 4) 거리 및유사도 계산 - 사용자 함수 df = cbrUsrFunction(oCR, nCR , getDistVar) # 5) 결과 df[df$sel_sim=='bestSimularity',]※ 위에서 사용된 사용자함수 링크 link text
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결과 - 기존과 가장 유사사례
x.n1 y.n1 diff.n1 x.n2 y.n2 diff.n2 x.n3 y.n3 diff.n3 x.n4 y.n4 diff.n4 x.n5 y.n5 diff.n5 x.n6 y.n6 diff.n6 x.c1 y.c1 diff.c1 x.c2 y.c2 diff.c2 1 0.00 0.00 0.00 0.23 0.23 0 0.23 0.23 0 0.55 0.54 0.002307692 0.04 0.04 0 0.00 0.00 0 f f 0 1 1 0 12 0.10 0.00 0.01 0.38 0.38 0 0.38 0.38 0 0.55 0.54 0.002307692 0.06 0.06 0 0.06 0.06 0 m m 0 1 1 0 23 0.00 0.00 0.00 0.08 0.08 0 0.08 0.08 0 0.55 0.54 0.002307692 0.48 0.48 0 0.04 0.04 0 f f 0 1 1 0 34 0.25 0.25 0.00 0.23 0.23 0 0.38 0.38 0 0.50 0.50 0.000000000 0.34 0.34 0 0.09 0.09 0 f f 0 1 1 0 45 0.00 0.00 0.00 1.00 1.00 0 1.00 1.00 0 0.55 0.54 0.002307692 0.33 0.33 0 0.01 0.01 0 m m 0 1 1 0 56 0.10 0.00 0.01 1.00 1.00 0 1.00 1.00 0 0.55 0.54 0.002307692 0.33 0.33 0 0.01 0.01 0 f f 0 1 1 0 67 0.25 0.25 0.00 0.46 0.46 0 0.46 0.46 0 0.53 0.53 0.000000000 0.19 0.19 0 0.08 0.08 0 f f 0 1 1 0 78 0.00 0.00 0.00 1.00 1.00 0 1.00 1.00 0 0.65 0.65 0.000000000 0.19 0.19 0 0.00 0.00 0 m m 0 1 1 0 89 0.00 0.00 0.00 0.62 0.62 0 0.62 0.62 0 0.53 0.53 0.000000000 0.19 0.19 0 0.00 0.00 0 m m 0 1 1 0 100 0.10 0.00 0.01 0.62 0.62 0 0.69 0.69 0 0.55 0.54 0.002307692 0.32 0.32 0 0.10 0.10 0 m m 0 1 1 0 x.target y.target diff.total Simularity x_idx y_idx sel_sim 1 0 0 0.002307692 0.9976976 o1 n1 bestSimularity 12 0 0 0.012307692 0.9878419 o2 n2 bestSimularity 23 0 0 0.002307692 0.9976976 o3 n3 bestSimularity 34 0 0 0.000000000 1.0000000 o4 n4 bestSimularity 45 0 0 0.002307692 0.9976976 o5 n5 bestSimularity 56 0 0 0.012307692 0.9878419 o6 n6 bestSimularity 67 1 1 0.000000000 1.0000000 o7 n7 bestSimularity 78 0 0 0.000000000 1.0000000 o8 n8 bestSimularity 89 0 0 0.000000000 1.0000000 o9 n9 bestSimularity 100 0 0 0.012307692 0.9878419 o10 n10 bestSimularity
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