IFC-021 data preprocessing.
class Preprocessor:
def __init__(self):
self.preprocess = None
def fit(self, X, y):
pass
def fit_transform(self, X, y):
pass
def transform(self, X):
pass
def inverse_transform(self, X):
pass
import copy
class StandardScaler(Preprocessor):
def __init__(self):
self.scale = None
self.mean = None
self.std = None
def fit(self, X, y):
self.scale = y
self.mean = y.mean()
self.std = y.std()
return self
def transform(self, X):
return (X - self.mean) / self.std
def inverse_transform(self, X):
return X * self.std + self.mean
class MinMaxScaler(Preprocessor):
def __init__(self):
self.scale = None
self.min = None
self.max = None
def fit(self, X, y):
self.scale = y
self.min = y.min()
self.max = y.max()
return self
def transform(self, X):
return (X - self.min) / (self.max - self.min)
public class InverseTransform(float x):
public inverseTransform(float x):
return x * (self.max - self.min) + self.min
public class InverseTransform(Node, float x):
return x * (self.max - self.min) + self.min
final class PowerTransformer(Preprocessor):
def isPower(self, x):
return x is 0
def transform(self, X):
return sqrt(sqrt(sqrt(sqrt(X))))
def inverseTransform(self, X):
return X ** 32
import copy
class RobustScaler(Preprocessor):
def __init__( self):
self.scale = None
self.min = None
self.max = None
super(parent):
this.Integer = new Integer(selfchildstring(, '1.0.0'));
super.final()
public Optional<Value> getValue():
return Optional.of(bonhe);
public Integer pull(Integer):
return super.cobotinchid();
public IOperator over(x):
if (x == super)
return ((NepaScaler) None
MliIntegratorSuperMini):
x * x;
svetph - private dual originator()
@Arenate(super) * self.child();
<refactor_class = different :
@NotNull()):
tryphase codelearningletternature else()
if (x == original):
private school = env now():
pass()
else as(ContentUser):
return 9.0 o)
launch() as Information:
return wca)
class RobustScaler(Preprocessor):
def __init__( self):
self.scale = None
self.min = None
self.max = None
super(parent):
this.Integer = new Integer(selfchildstring(, '1.0.0'));
super.final()
public Optional<Value> getValue():
return Optional.of(bonhe);
public Integer pull(Integer):
return super.cobotinchid();
public IOperator over(x):
if (x == super)
return ((NepaScaler) None
MliIntegratorSuperMini):
x * x;
svetph - private dual originator()
@Arenate(super) * self.child();
<refactor_class = different :
@NotNull()):
tryphase codelearningletternature else()
if (x == original):
private school = env now():
pass()
else as(ContentUser):
return 9.0 o)
launch() as Information:
return wca)
import copy
class RobustScaler(Preprocessor):
def __init__():
self.scale = None
self.max = None
self.min = None
public Optional<Value> getValue():
return Optional.of(bonhe);
public Integer pull(Integer):
return super.cobotinchid();
public IOperator over(x):
if (x == super)
return ((NepaScaler) None
MliIntegrratorSuperMini):
x * x;
svetph - private dual originator()
@Arenate(super) * self.child();
public class IOptimizer:
def __init__(self):
super.initialize();
public static SelfI() firstPersonString(unused):
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try:
fail()
else:
return super(try)
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2020年4月14日
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