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Transforming a sql table into its inverse


By : user2174652
Date : October 17 2020, 08:10 PM
Does that help Kind of a unusual requirement if these are the actual data.
Anyway, do a SELF CROSS JOIN or INNER JOIN on the same table and check for State not match
code :
SELECT  DISTINCT sc1.State, sc2.City
FROM    #State_City sc1
        INNER JOIN #State_City sc2  on  sc1.State   <> sc2.State
ORDER BY sc1.State, sc2.City    


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Why transforming normals with the transpose of the inverse of the modelview matrix?


By : Sheay Tam
Date : March 29 2020, 07:55 AM

Inverse of 'concat': transforming a flattened list into a nested list


By : MAHESH
Date : March 29 2020, 07:55 AM
hop of those help? Given a flattened list in Haskell: , You can do:
code :
singleton x = [x]
f = map singleton

Using inverse function with transforming geometry into coordinates results into mixed SRID error


By : Rajnish
Date : March 29 2020, 07:55 AM
To fix this issue The problem is somewhat described in the error message: you are trying to find equal geometries in planet_osm_point.way, but they have a different SRID than 4674. It is effectively this:
code :
SELECT ST_SetSRID(ST_Point(0, 0), 4674) = ST_SetSRID(ST_Point(0, 0), 4326);
SELECT ST_SetSRID(ST_Point(0, 0), 4674) =
                      ST_Transform(ST_SetSRID(ST_Point(0, 0), 4326), 4674);
select st_x(st_transform(way,4326)), st_y(st_transform(way,4326))
from planet_osm_point
where ST_DWithin(
         way,
         ST_Transform(
           ST_SetSRID(ST_Point(18.533935546875, 48.23199134320962), 4674),
           <way_SRID>),
         1e-6);

Numpy: Inverse Transforming Different Size Array


By : Nikolay S Sudakov
Date : March 29 2020, 07:55 AM
With these it helps You don't have to pass a data set with exactly the same shape, but the number of columns must match the original data set as each row is interpreted as a record and each column is interpreted as a feature. And you can not miss features for your testing data set, technically. So for instance, slicing the rows will still work:
code :
new_dataset = dataset[:1,:]
trainPredict2 = scaler.inverse_transform(new_dataset)
trainPredict2
# array([[ 1.2345 ,  1.22   ,  1.26245]])
scaler.data_range_[:1] * dataset[:,:1] + scaler.data_min_[:1]
# array([[ 1.2345],
#        [ 1.234 ],
#        [ 1.2345]])

MultiLabelBinarizer mixes up data when inverse transforming


By : user1646731
Date : March 29 2020, 07:55 AM
it helps some times The issue comes from the data (and in this case a bad use of the model). If you create a Dataframe of your MultiLabelBinarizer you will have :
code :
import pandas as pd
import numpy as np
from sklearn.preprocessing import MultiLabelBinarizer
from collections import defaultdict

data = {
    "State" : ["California", "New York", "Alaska", "Arizona", "Alaska", "Arizona"],
    "Month" : ["January", "February", "May", "February", "January", "February" ],
    "Number" : ["1000", "750", "500", "25000", "2000", "1"]
}
df = pd.DataFrame(data)

d = defaultdict(MultiLabelBinarizer)  # dict of Features => model

list_encoded = []  # store single matrices
for column in df:
    d[column].fit(df[column])
    list_encoded.append(d[column].transform(df[column]))
merged = np.hstack(list_encoded) # matrix of 6 x 32
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