Commit 1a2bcd4b authored by Antoine Regimbeau's avatar Antoine Regimbeau

TEST: second part import all baseline but tif

parent 6967a30a
Forward UTM projection:
[1.4835345, 43.55968261] -> [377522.448427013, 4824086.71129131]
[1.4835345, 43.55968261] -> [377522.448427348, 4824086.71134283]
Forward Lambert93 projection:
[1.4835345, 43.55968261] -> [577437.889798954, 6274578.791561]
Forward generic projection:
[1.4835345, 43.55968261] -> [377522.448427013, 4824086.71129131]
[1.4835345, 43.55968261] -> [577437.889799322, 6274578.79155995]
<?xml version="1.0" ?>
<GeneralStatistics>
<Statistic name="samplesPerClass">
<StatisticMap key="1" value="4238" />
<StatisticMap key="2" value="563" />
<StatisticMap key="3" value="9561" />
<StatisticMap key="4" value="85" />
<StatisticMap key="1" value="4244" />
<StatisticMap key="2" value="564" />
<StatisticMap key="3" value="9560" />
<StatisticMap key="4" value="87" />
</Statistic>
<Statistic name="samplesPerVector">
<StatisticMap key="0" value="4238" />
<StatisticMap key="1" value="563" />
<StatisticMap key="2" value="9561" />
<StatisticMap key="3" value="85" />
<StatisticMap key="1" value="4244" />
<StatisticMap key="2" value="564" />
<StatisticMap key="3" value="9560" />
<StatisticMap key="4" value="87" />
</Statistic>
</GeneralStatistics>
#Reference labels (rows):1,2,3,4
#Produced labels (columns):1,2,3,4
42,0,0,0
0,42,0,0
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#Reference labels (rows):1,2,3,4
#Produced labels (columns):1,2,3,4
42,0,0,0
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......@@ -12,198 +12,198 @@ my_nb: !!opencv-ml-bayesian
rows: 1
cols: 4
dt: i
data: [ 43, 43, 43, 43 ]
data: [ 45, 45, 45, 45 ]
- !!opencv-matrix
rows: 1
cols: 4
dt: i
data: [ 43, 43, 43, 43 ]
data: [ 45, 45, 45, 45 ]
- !!opencv-matrix
rows: 1
cols: 4
dt: i
data: [ 43, 43, 43, 43 ]
data: [ 45, 45, 45, 45 ]
- !!opencv-matrix
rows: 1
cols: 4
dt: i
data: [ 43, 43, 43, 43 ]
data: [ 45, 45, 45, 45 ]
sum:
- !!opencv-matrix
rows: 1
cols: 4
dt: d
data: [ -2.1922148197889328e+01, -2.7578045368194580e+01,
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data: [ -2.1993521749973297e+01, -2.8384285211563110e+01,
-4.0851101338863373e+01, -7.6161080718040466e+01 ]
- !!opencv-matrix
rows: 1
cols: 4
dt: d
data: [ 1.0171979464590549e+01, -3.2993363565765321e-01,
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data: [ 1.1852214407175779e+01, 2.1828738153853919e+00,
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- !!opencv-matrix
rows: 1
cols: 4
dt: d
data: [ -2.6771352648735046e+01, -2.0587651997804642e+01,
-2.3959105100482702e+01, 5.0243718490004539e+01 ]
data: [ -2.6466631185496226e+01, -2.0262194167822599e+01,
-2.4436924412846565e+01, 5.3284225802868605e+01 ]
- !!opencv-matrix
rows: 1
cols: 4
dt: d
data: [ 2.7658943295478821e+01, 5.2530834734439850e+01,
1.0023734784126282e+02, 2.2044248998165131e+01 ]
data: [ 2.8748684972524643e+01, 5.5096173614263535e+01,
1.0371144127845764e+02, 2.2969044992700219e+01 ]
productsum:
- !!opencv-matrix
rows: 4
cols: 4
dt: d
data: [ 1.1334907806227342e+01, 1.4106713934400750e+01,
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data: [ 1.0931021061344623e+01, 1.3932592010588799e+01,
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- !!opencv-matrix
rows: 4
cols: 4
dt: d
data: [ 2.8536261169779271e+00, 2.7449107020275826e-01,
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3.6893748841948659e-01, 5.6116084453262582e-01,
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data: [ 3.4886572275727756e+00, 9.8869695434280458e-01,
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-1.1533777357248479e+01, -1.6210542989008394e+00,
1.7844632026252991e+00, 4.6702135952001868e+01 ]
- !!opencv-matrix
rows: 4
cols: 4
dt: d
data: [ 1.8361237094424304e+01, 1.4983181689856238e+01,
1.7045058630829118e+01, -2.9246173065404498e+01,
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1.4481228161753974e+01, -2.0114496856277562e+01,
1.7045058630829118e+01, 1.4481228161753974e+01,
1.6530650065412114e+01, -2.6706934624693098e+01,
-2.9246173065404498e+01, -2.0114496856277562e+01,
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data: [ 1.7419171968756206e+01, 1.3921320045889088e+01,
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1.3771137662556043e+01, -2.4706907265536955e+01,
1.6782085983540895e+01, 1.3771137662556043e+01,
1.6885467707064716e+01, -3.3243810638877996e+01,
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-3.3243810638877996e+01, 9.1336598494961137e+01 ]
- !!opencv-matrix
rows: 4
cols: 4
dt: d
data: [ 2.1046145088390379e+01, 3.8424028927514179e+01,
7.0411584893979324e+01, 1.8052737808870056e+01,
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1.3217795888927964e+02, 3.3130444244416317e+01,
7.0411584893979324e+01, 1.3217795888927964e+02,
2.4793842825094077e+02, 6.0087130090913739e+01,
1.8052737808870056e+01, 3.3130444244416317e+01,
6.0087130090913739e+01, 1.6970155373076185e+01 ]
data: [ 2.1738991185265157e+01, 4.0097965609184804e+01,
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4.0097965609184804e+01, 7.5346369171722728e+01,
1.3735549805366293e+02, 3.4845481105752164e+01,
7.2606272827508491e+01, 1.3735549805366293e+02,
2.5393415462855313e+02, 6.1974353708925271e+01,
1.8884162486217626e+01, 3.4845481105752164e+01,
6.1974353708925271e+01, 1.8009656142785030e+01 ]
avg:
- !!opencv-matrix
rows: 1
cols: 4
dt: d
data: [ -5.0981739995091457e-01, -6.4134989228359485e-01,
-9.0219729029855067e-01, -1.6868821798368943e+00 ]
data: [ -4.8874492777718437e-01, -6.3076189359029133e-01,
-9.0780225197474163e-01, -1.6924684604008993e+00 ]
- !!opencv-matrix
rows: 1
cols: 4
dt: d
data: [ 2.3655766196722208e-01, -7.6728752478524001e-03,
-8.2681287312880158e-02, -1.0751096744870030e+00 ]
data: [ 2.6338254238168396e-01, 4.8508307008564268e-02,
-2.1872603986412286e-02, -1.0094273871845669e+00 ]
- !!opencv-matrix
rows: 1
cols: 4
dt: d
data: [ -6.2258959648221035e-01, -4.7878260460010796e-01,
-5.5718849070890009e-01, 1.1684585695349894e+00 ]
data: [ -5.8814735967769394e-01, -4.5027098150716888e-01,
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- !!opencv-matrix
rows: 1
cols: 4
dt: d
data: [ 6.4323123942974003e-01, 1.2216473194055779e+00,
2.3311011125875072e+00, 5.1265695344570072e-01 ]
data: [ 6.3885966605610323e-01, 1.2243594136503009e+00,
2.3046986950768367e+00, 5.1042322206000490e-01 ]
inv_eigen_values:
- !!opencv-matrix
rows: 1
cols: 4
dt: d
data: [ 1.7036290508681049e+02, 2.7444604480428717e+02,
6.5083493560395721e+02, 3.0052952245071065e+03 ]
data: [ 1.7698076305933722e+02, 3.7746235697487452e+02,
5.7039067482115001e+02, 2.4177971123851048e+03 ]
- !!opencv-matrix
rows: 1
cols: 4
dt: d
data: [ 3.5198919026378150e+01, 4.1303600041327149e+02,
7.2651362667433921e+02, 1.3331772008976288e+03 ]
data: [ 1.7228020481698010e+01, 2.0722715247052972e+02,
4.7779223904670704e+02, 7.5785542050956508e+02 ]
- !!opencv-matrix
rows: 1
cols: 4
dt: d
data: [ 1.5177841661358380e+00, 6.1140335203919873e+00,
2.4593229198916495e+02, 6.3337767010678772e+02 ]
data: [ 1.5039441374768798e+00, 6.4770979912640749e+00,
2.9538485442619515e+02, 4.7103877483924140e+02 ]
- !!opencv-matrix
rows: 1
cols: 4
dt: d
data: [ 1.4315716189687104e+00, 4.9513712463596995e+01,
1.9677065140428138e+02, 2.5476254270295979e+02 ]
data: [ 1.4143681718516696e+00, 5.6617599680224615e+01,
1.1071231935141257e+02, 2.5308819611786578e+02 ]
cov_rotate_mats:
- !!opencv-matrix
rows: 4
cols: 4
dt: d
data: [ 4.1695057889651599e-01, 6.4872584607309292e-01,
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3.2719332438314597e-01, -7.5998937214176687e-01,
5.2839992350979270e-01, 1.9014258740617218e-01,
9.2888283415050593e-02, 2.7143304204536048e-02,
-3.5331022294149550e-01, 9.3048744978430320e-01 ]
data: [ 7.1737977751325399e-01, 5.4992261926702235e-01,
4.0127183164892033e-01, 1.4809552578877000e-01,
-6.4588324852860823e-01, 3.0021038845648496e-01,
6.2626113067205036e-01, 3.1702610009635374e-01,
2.5999085095076890e-01, -7.7716045882844476e-01,
4.9147124258388614e-01, 2.9475819983251911e-01,
2.4613847871904376e-02, 5.8995395709030625e-02,
-4.5302751687677478e-01, 8.8920176041803756e-01 ]
- !!opencv-matrix
rows: 4
cols: 4
dt: d
data: [ 5.6331388567005680e-01, 5.6700602287854340e-01,
5.0529676628764564e-01, 3.2535644178348067e-01,
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4.3698132566746828e-01, 1.8587367652222322e-01,
1.1070747851106079e-02, -5.4847147103206251e-01,
6.6459676893769637e-02, 8.3345041565357603e-01,
-9.8582413626951415e-02, 5.2537732208972698e-01,
-7.4115154344056122e-01, 4.0614599199424095e-01 ]
data: [ 3.3368736795205495e-01, 4.9506944360196226e-01,
6.0041545741241886e-01, 5.3203408253711448e-01,
-8.1924523688589670e-02, -6.6348337619629683e-01,
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2.4340284541973972e-01, 1.1181128749601008e-01,
3.0137337066751219e-02, -5.1718045061168016e-01,
7.5905647643234708e-01, -3.9427070396585906e-01 ]
- !!opencv-matrix
rows: 4
cols: 4
dt: d
data: [ 9.7834578499564964e-02, 1.7628233389972503e-01,
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data: [ -9.1631879500872565e-02, -5.1617229134840556e-02,
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3.6699530575216255e-01, 8.8141530205549279e-04,
2.2224886588655429e-02, -7.3794751066554609e-01,
6.6908118906554948e-01, 8.5263640300396931e-02 ]
- !!opencv-matrix
rows: 4
cols: 4
dt: d
data: [ 3.0859729677305181e-01, 4.9224831904233540e-01,
6.8930728884598891e-01, 4.3279875504459658e-01,
-6.7341152277812066e-01, -4.3805358765120350e-01,
5.9468443772075763e-01, 3.1247317777823819e-02,
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data: [ 3.1032973829968136e-01, 4.9694383958708122e-01,
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7.0302989938870719e-01, -3.8514030453482839e-01,
-5.2453183693260685e-01, -2.8210197088669159e-01,
-6.7322457629632321e-02, 8.0047018480681442e-01,
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c: !!opencv-matrix
rows: 1
cols: 4
dt: d
data: [ -2.5239072722573727e+01, -2.3368127614578629e+01,
-1.4183961333534718e+01, -1.5083393276383324e+01 ]
data: [ -2.5246445531409311e+01, -2.0980021323588918e+01,
-1.4119583163904649e+01, -1.4623675943683679e+01 ]
#Reference labels (rows):1,3
#Produced labels (columns):1,3
2122,0
0,2122
#Reference labels (rows):1,2,3,4
#Produced labels (columns):1,2,3,4
42,0,0,0
0,41,1,0
0,0,42,0
0,0,0,42
#Reference labels (rows):1,2,3,4
#Produced labels (columns):1,2,3,4
42,0,0,0
0,41,1,0
0,0,42,0
0,0,0,42
......@@ -33,33 +33,33 @@ my_tree: !!opencv-ml-tree
nodes:
-
depth: 0
sample_count: 172
sample_count: 180
value: 1.
norm_class_idx: 0
Tn: 0
complexity: 4
alpha: 43.
node_risk: 129.
alpha: 45.
node_risk: 135.
tree_risk: 0.
tree_error: 0.
splits:
- { var:0, quality:86., le:-8.9000031352043152e-02 }
- { var:0, quality:90., le:2.4664748460054398e-02 }
-
depth: 1
sample_count: 86
sample_count: 90
value: 1.
norm_class_idx: 0
Tn: 2147483647
complexity: 2
alpha: 43.
node_risk: 43.
alpha: 45.
node_risk: 45.
tree_risk: 0.
tree_error: 0.
splits:
- { var:3, quality:86., le:-1.3876452445983887e+00 }
- { var:3, quality:90., le:-1.2730357646942139e+00 }
-
depth: 2
sample_count: 43
sample_count: 45
value: 1.
norm_class_idx: 0
Tn: 2147483647
......@@ -70,7 +70,7 @@ my_tree: !!opencv-ml-tree
tree_error: 0.
-
depth: 2
sample_count: 43
sample_count: 45
value: 3.
norm_class_idx: 2
Tn: 2147483647
......@@ -81,20 +81,20 @@ my_tree: !!opencv-ml-tree
tree_error: 0.
-
depth: 1
sample_count: 86
sample_count: 90
value: 2.
norm_class_idx: 1
Tn: 2147483647
complexity: 2
alpha: 43.
node_risk: 43.
alpha: 45.
node_risk: 45.
tree_risk: 0.
tree_error: 0.
splits:
- { var:1, quality:86., le:3.4949243068695068e-01 }
- { var:2, quality:90., le:8.2657122611999512e-01 }
-
depth: 2
sample_count: 43
sample_count: 45
value: 2.
norm_class_idx: 1
Tn: 2147483647
......@@ -105,7 +105,7 @@ my_tree: !!opencv-ml-tree
tree_error: 0.
-
depth: 2
sample_count: 43
sample_count: 45
value: 4.
norm_class_idx: 3
Tn: 2147483647
......
#Reference labels (rows):1,2,3,4
#Produced labels (columns):1,2,3,4
42,0,0,0
0,41,1,0
0,0,42,0
0,0,0,42
This diff is collapsed.
#Reference labels (rows):1,2,3,4
#Produced labels (columns):1,2,3,4
42,0,0,0
0,42,0,0
3,3,36,0
0,0,0,42
K=32
IsRegression=0
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