# FILE SS_All_model.txt
# === Run information ===
# Scheme:       weka.classifiers.trees.M5P -M 4.0 -num-decimal-places 4
# Relation:     F2022Assn3Keepers-weka.filters.unsupervised.attribute.Remove-R46-47
# Instances:    226
# Attributes:   45
# year
# month
# HMtempC_mean
# WindSpd_mean
# HMtempC_median
# WindSpd_median
# HMtempC_pstdv
# WindSpd_pstdv
# HMtempC_min
# WindSpd_min
# HMtempC_max
# WindSpd_max
# wndN
# wndNNE
# wndNE
# wndENE
# wndE
# wndESE
# wndSE
# wndSSE
# wndS
# wndSSW
# wndSW
# wndWSW
# wndW
# wndWNW
# wndNW
# wndNNW
# wndUNK
# HMtempC_24_mean
# HMtempC_48_mean
# HMtempC_72_mean
# HMtempC_24_median
# HMtempC_48_median
# HMtempC_72_median
# HMtempC_24_pstdv
# HMtempC_48_pstdv
# HMtempC_72_pstdv
# HMtempC_24_min
# HMtempC_48_min
# HMtempC_72_min
# HMtempC_24_max
# HMtempC_48_max
# HMtempC_72_max
# SS_All
# Test mode:    10-fold cross-validation
# === Classifier model (full training set) ===
# M5 pruned model tree:
# (using smoothed linear models)
## month <= 10.5 :
## |   month <= 8.5 : LM1 (44/1.385%)
## |   month >  8.5 :
## |   |   month <= 9.5 : LM2 (46/29.571%)
## |   |   month >  9.5 :
## |   |   |   year <= 1991.5 : LM3 (16/49.535%)
## |   |   |   year >  1991.5 :
## |   |   |   |   wndESE <= 1.5 : LM4 (13/26.17%)
## |   |   |   |   wndESE >  1.5 :
## |   |   |   |   |   WindSpd_mean <= 9.647 : LM5 (9/24.311%)
## |   |   |   |   |   WindSpd_mean >  9.647 : LM6 (8/23.164%)
## month >  10.5 : LM7 (90/4.821%)
## LM num: 1
## SS_All =
## -4.4828 * year
## + 463.8708 * month
## - 4.7031 * HMtempC_mean
## + 21.9141 * WindSpd_mean
## + 1.3857 * wndN
## - 12.3917 * wndNE
## + 4.1044 * wndE
## - 15.2088 * wndESE
## - 0.6987 * wndS
## - 0.729 * wndW
## + 1.9376 * wndWNW
## + 1.4007 * wndNW
## - 11.8282 * HMtempC_48_min
## + 5112.7772
## LM num: 2
## SS_All =
## -27.3529 * year
## + 829.5192 * month
## + 9.4129 * HMtempC_mean
## + 48.1933 * WindSpd_mean
## - 16.7184 * WindSpd_pstdv
## + 1.3857 * wndN
## - 6.8328 * wndNE
## + 4.1044 * wndE
## - 8.3862 * wndESE
## + 0.6357 * wndS
## + 0.6085 * wndW
## + 1.9376 * wndWNW
## + 8.2414 * wndNW
## - 19.4016 * HMtempC_48_min
## + 47143.49
## LM num: 3
## SS_All =
## -25.3474 * year
## + 829.5192 * month
## + 59.2934 * HMtempC_mean
## + 289.988 * WindSpd_mean
## - 123.0157 * WindSpd_median
## + 436.674 * HMtempC_pstdv
## - 138.4052 * WindSpd_pstdv
## + 11.9276 * wndN
## - 6.8328 * wndNE
## + 4.1044 * wndE
## - 8.3862 * wndESE
## + 0.6357 * wndS
## + 0.6085 * wndW
## + 1.9376 * wndWNW
## + 3.045 * wndNW
## - 19.4016 * HMtempC_48_min
## - 231.5027 * HMtempC_72_min
## + 39574.6889
## LM num: 4
## SS_All =
## -21.2432 * year
## + 829.5192 * month
## + 78.5681 * HMtempC_mean
## + 214.763 * WindSpd_mean
## - 27.6933 * WindSpd_median
## + 72.8137 * HMtempC_pstdv
## - 100.5471 * WindSpd_pstdv
## - 0.2516 * wndN
## - 6.8328 * wndNE
## + 4.1044 * wndE
## - 21.5765 * wndESE
## + 0.6357 * wndS
## + 0.6085 * wndW
## + 1.9376 * wndWNW
## + 3.045 * wndNW
## + 47.8059 * HMtempC_24_mean
## - 19.4016 * HMtempC_48_min
## - 38.1691 * HMtempC_72_min
## + 34271.7562
## LM num: 5
## SS_All =
## -21.2432 * year
## + 829.5192 * month
## + 74.219 * HMtempC_mean
## + 258.6552 * WindSpd_mean
## - 34.8247 * WindSpd_median
## + 72.8137 * HMtempC_pstdv
## - 126.7053 * WindSpd_pstdv
## + 8.6479 * wndN
## - 6.8328 * wndNE
## + 4.1044 * wndE
## - 19.9277 * wndESE
## + 0.6357 * wndS
## + 0.6085 * wndW
## + 1.9376 * wndWNW
## + 3.045 * wndNW
## + 6.8966 * HMtempC_48_min
## - 38.1691 * HMtempC_72_min
## + 34241.6592
## LM num: 6
## SS_All =
## -21.2432 * year
## + 829.5192 * month
## + 74.219 * HMtempC_mean
## + 260.5636 * WindSpd_mean
## - 34.8247 * WindSpd_median
## + 72.8137 * HMtempC_pstdv
## - 148.9523 * WindSpd_pstdv
## + 8.6479 * wndN
## - 6.8328 * wndNE
## + 4.1044 * wndE
## - 19.9277 * wndESE
## + 0.6357 * wndS
## + 0.6085 * wndW
## + 1.9376 * wndWNW
## + 3.045 * wndNW
## - 2.7562 * HMtempC_48_min
## - 38.1691 * HMtempC_72_min
## + 34376.948
## LM num: 7
## SS_All =
## -6.4467 * year
## - 41.5321 * month
## - 6.7635 * HMtempC_mean
## + 1.9928 * wndN
## + 5.9025 * wndE
## + 3.273 * wndS
## + 3.2392 * wndW
## + 2.7865 * wndWNW
## + 2.0144 * wndNW
## + 13239.0407

# NAME=SS_All_model
# TARGET=SS_All
# ATTRIBUTES=
#     HMtempC_24_mean
#     HMtempC_48_min
#     HMtempC_72_min
#     HMtempC_mean
#     HMtempC_pstdv
#     WindSpd_mean
#     WindSpd_median
#     WindSpd_pstdv
#     month
#     wndE
#     wndESE
#     wndN
#     wndNE
#     wndNW
#     wndS
#     wndW
#     wndWNW
#     year
# TESTS=
#     (0, 'month', '<=', 10.5, None)
#     (1, 'month', '<=', 8.5, 1)
#     (1, 'month', '>', 8.5, None)
#     (2, 'month', '<=', 9.5, 2)
#     (2, 'month', '>', 9.5, None)
#     (3, 'year', '<=', 1991.5, 3)
#     (3, 'year', '>', 1991.5, None)
#     (4, 'wndESE', '<=', 1.5, 4)
#     (4, 'wndESE', '>', 1.5, None)
#     (5, 'WindSpd_mean', '<=', 9.647, 5)
#     (5, 'WindSpd_mean', '>', 9.647, 6)
#     (0, 'month', '>', 10.5, 7)
# LINEAR_EXPRESSIONS=
#     1=SS_All=-4.4828*year+463.8708*month-4.7031*HMtempC_mean+21.9141*WindSpd_mean+1.3857*wndN-12.3917*wndNE+4.1044*wndE-15.2088*wndESE-0.6987*wndS-0.729*wndW+1.9376*wndWNW+1.4007*wndNW-11.8282*HMtempC_48_min+5112.7772
#     2=SS_All=-27.3529*year+829.5192*month+9.4129*HMtempC_mean+48.1933*WindSpd_mean-16.7184*WindSpd_pstdv+1.3857*wndN-6.8328*wndNE+4.1044*wndE-8.3862*wndESE+0.6357*wndS+0.6085*wndW+1.9376*wndWNW+8.2414*wndNW-19.4016*HMtempC_48_min+47143.49
#     3=SS_All=-25.3474*year+829.5192*month+59.2934*HMtempC_mean+289.988*WindSpd_mean-123.0157*WindSpd_median+436.674*HMtempC_pstdv-138.4052*WindSpd_pstdv+11.9276*wndN-6.8328*wndNE+4.1044*wndE-8.3862*wndESE+0.6357*wndS+0.6085*wndW+1.9376*wndWNW+3.045*wndNW-19.4016*HMtempC_48_min-231.5027*HMtempC_72_min+39574.6889
#     4=SS_All=-21.2432*year+829.5192*month+78.5681*HMtempC_mean+214.763*WindSpd_mean-27.6933*WindSpd_median+72.8137*HMtempC_pstdv-100.5471*WindSpd_pstdv-0.2516*wndN-6.8328*wndNE+4.1044*wndE-21.5765*wndESE+0.6357*wndS+0.6085*wndW+1.9376*wndWNW+3.045*wndNW+47.8059*HMtempC_24_mean-19.4016*HMtempC_48_min-38.1691*HMtempC_72_min+34271.7562
#     5=SS_All=-21.2432*year+829.5192*month+74.219*HMtempC_mean+258.6552*WindSpd_mean-34.8247*WindSpd_median+72.8137*HMtempC_pstdv-126.7053*WindSpd_pstdv+8.6479*wndN-6.8328*wndNE+4.1044*wndE-19.9277*wndESE+0.6357*wndS+0.6085*wndW+1.9376*wndWNW+3.045*wndNW+6.8966*HMtempC_48_min-38.1691*HMtempC_72_min+34241.6592
#     6=SS_All=-21.2432*year+829.5192*month+74.219*HMtempC_mean+260.5636*WindSpd_mean-34.8247*WindSpd_median+72.8137*HMtempC_pstdv-148.9523*WindSpd_pstdv+8.6479*wndN-6.8328*wndNE+4.1044*wndE-19.9277*wndESE+0.6357*wndS+0.6085*wndW+1.9376*wndWNW+3.045*wndNW-2.7562*HMtempC_48_min-38.1691*HMtempC_72_min+34376.948
#     7=SS_All=-6.4467*year-41.5321*month-6.7635*HMtempC_mean+1.9928*wndN+5.9025*wndE+3.273*wndS+3.2392*wndW+2.7865*wndWNW+2.0144*wndNW+13239.0407

def make_SS_All_model(attrNamesToColumns):
    HMtempC_24_mean_COLUMN = attrNamesToColumns["HMtempC_24_mean"]
    HMtempC_48_min_COLUMN = attrNamesToColumns["HMtempC_48_min"]
    HMtempC_72_min_COLUMN = attrNamesToColumns["HMtempC_72_min"]
    HMtempC_mean_COLUMN = attrNamesToColumns["HMtempC_mean"]
    HMtempC_pstdv_COLUMN = attrNamesToColumns["HMtempC_pstdv"]
    WindSpd_mean_COLUMN = attrNamesToColumns["WindSpd_mean"]
    WindSpd_median_COLUMN = attrNamesToColumns["WindSpd_median"]
    WindSpd_pstdv_COLUMN = attrNamesToColumns["WindSpd_pstdv"]
    month_COLUMN = attrNamesToColumns["month"]
    wndE_COLUMN = attrNamesToColumns["wndE"]
    wndESE_COLUMN = attrNamesToColumns["wndESE"]
    wndN_COLUMN = attrNamesToColumns["wndN"]
    wndNE_COLUMN = attrNamesToColumns["wndNE"]
    wndNW_COLUMN = attrNamesToColumns["wndNW"]
    wndS_COLUMN = attrNamesToColumns["wndS"]
    wndW_COLUMN = attrNamesToColumns["wndW"]
    wndWNW_COLUMN = attrNamesToColumns["wndWNW"]
    year_COLUMN = attrNamesToColumns["year"]
    def SS_All_model(rowOfData):
        HMtempC_24_mean= rowOfData[HMtempC_24_mean_COLUMN]
        HMtempC_48_min= rowOfData[HMtempC_48_min_COLUMN]
        HMtempC_72_min= rowOfData[HMtempC_72_min_COLUMN]
        HMtempC_mean= rowOfData[HMtempC_mean_COLUMN]
        HMtempC_pstdv= rowOfData[HMtempC_pstdv_COLUMN]
        WindSpd_mean= rowOfData[WindSpd_mean_COLUMN]
        WindSpd_median= rowOfData[WindSpd_median_COLUMN]
        WindSpd_pstdv= rowOfData[WindSpd_pstdv_COLUMN]
        month= rowOfData[month_COLUMN]
        wndE= rowOfData[wndE_COLUMN]
        wndESE= rowOfData[wndESE_COLUMN]
        wndN= rowOfData[wndN_COLUMN]
        wndNE= rowOfData[wndNE_COLUMN]
        wndNW= rowOfData[wndNW_COLUMN]
        wndS= rowOfData[wndS_COLUMN]
        wndW= rowOfData[wndW_COLUMN]
        wndWNW= rowOfData[wndWNW_COLUMN]
        year= rowOfData[year_COLUMN]
        if (month <= 10.5):
            if (month <= 8.5):
                SS_All=-4.4828*year+463.8708*month-4.7031*HMtempC_mean+21.9141*WindSpd_mean+1.3857*wndN-12.3917*wndNE+4.1044*wndE-15.2088*wndESE-0.6987*wndS-0.729*wndW+1.9376*wndWNW+1.4007*wndNW-11.8282*HMtempC_48_min+5112.7772
            else:  #elif (month > 8.5):
                if (month <= 9.5):
                    SS_All=-27.3529*year+829.5192*month+9.4129*HMtempC_mean+48.1933*WindSpd_mean-16.7184*WindSpd_pstdv+1.3857*wndN-6.8328*wndNE+4.1044*wndE-8.3862*wndESE+0.6357*wndS+0.6085*wndW+1.9376*wndWNW+8.2414*wndNW-19.4016*HMtempC_48_min+47143.49
                else:  #elif (month > 9.5):
                    if (year <= 1991.5):
                        SS_All=-25.3474*year+829.5192*month+59.2934*HMtempC_mean+289.988*WindSpd_mean-123.0157*WindSpd_median+436.674*HMtempC_pstdv-138.4052*WindSpd_pstdv+11.9276*wndN-6.8328*wndNE+4.1044*wndE-8.3862*wndESE+0.6357*wndS+0.6085*wndW+1.9376*wndWNW+3.045*wndNW-19.4016*HMtempC_48_min-231.5027*HMtempC_72_min+39574.6889
                    else:  #elif (year > 1991.5):
                        if (wndESE <= 1.5):
                            SS_All=-21.2432*year+829.5192*month+78.5681*HMtempC_mean+214.763*WindSpd_mean-27.6933*WindSpd_median+72.8137*HMtempC_pstdv-100.5471*WindSpd_pstdv-0.2516*wndN-6.8328*wndNE+4.1044*wndE-21.5765*wndESE+0.6357*wndS+0.6085*wndW+1.9376*wndWNW+3.045*wndNW+47.8059*HMtempC_24_mean-19.4016*HMtempC_48_min-38.1691*HMtempC_72_min+34271.7562
                        else:  #elif (wndESE > 1.5):
                            if (WindSpd_mean <= 9.647):
                                SS_All=-21.2432*year+829.5192*month+74.219*HMtempC_mean+258.6552*WindSpd_mean-34.8247*WindSpd_median+72.8137*HMtempC_pstdv-126.7053*WindSpd_pstdv+8.6479*wndN-6.8328*wndNE+4.1044*wndE-19.9277*wndESE+0.6357*wndS+0.6085*wndW+1.9376*wndWNW+3.045*wndNW+6.8966*HMtempC_48_min-38.1691*HMtempC_72_min+34241.6592
                            else:  #elif (WindSpd_mean > 9.647):
                                SS_All=-21.2432*year+829.5192*month+74.219*HMtempC_mean+260.5636*WindSpd_mean-34.8247*WindSpd_median+72.8137*HMtempC_pstdv-148.9523*WindSpd_pstdv+8.6479*wndN-6.8328*wndNE+4.1044*wndE-19.9277*wndESE+0.6357*wndS+0.6085*wndW+1.9376*wndWNW+3.045*wndNW-2.7562*HMtempC_48_min-38.1691*HMtempC_72_min+34376.948
        else:  #elif (month > 10.5):
            SS_All=-6.4467*year-41.5321*month-6.7635*HMtempC_mean+1.9928*wndN+5.9025*wndE+3.273*wndS+3.2392*wndW+2.7865*wndWNW+2.0144*wndNW+13239.0407
        return SS_All
    return SS_All_model
target_SS_All_model = "SS_All"
attributes_SS_All_model = ["HMtempC_24_mean","HMtempC_48_min","HMtempC_72_min","HMtempC_mean","HMtempC_pstdv","WindSpd_mean","WindSpd_median","WindSpd_pstdv","month","wndE","wndESE","wndN","wndNE","wndNW","wndS","wndW","wndWNW","year"]

# Number of Rules : 7
# Time taken to build model: 0.02 seconds
# === Cross-validation ===
# === Summary ===
# Correlation coefficient                  0.9076
# Mean absolute error                    407.9356
# Root mean squared error                736.9916
# Relative absolute error                 31.0553 %
# Root relative squared error             42.2449 %
# Total Number of Instances              226