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Minor updates #53

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Minor updates #53

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4dcf793
Replaced np.float with float
davidstandingford Jan 19, 2023
110dad5
Replaced indeterminate if ... elif with if ... else
davidstandingford Jan 19, 2023
5ed30de
Replaced numpy.float with float
davidstandingford Jan 19, 2023
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6 changes: 3 additions & 3 deletions pyKriging/coKriging.py
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Original file line number Diff line number Diff line change
Expand Up @@ -111,9 +111,9 @@ def distanceXcXe(self):


def updatePsi(self):
self.PsicXc = np.zeros((self.nc,self.nc), dtype=np.float)
self.PsicXe = np.zeros((self.ne,self.ne), dtype=np.float)
self.PsicXcXe = np.zeros((self.nc,self.ne), dtype=np.float)
self.PsicXc = np.zeros((self.nc,self.nc), dtype=float)
self.PsicXe = np.zeros((self.ne,self.ne), dtype=float)
self.PsicXcXe = np.zeros((self.nc,self.ne), dtype=float)
#
# print self.thetac
# print self.pc
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4 changes: 2 additions & 2 deletions pyKriging/krige.py
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Original file line number Diff line number Diff line change
Expand Up @@ -208,7 +208,7 @@ def expimp(self, x):
y_min = np.min(self.y)
if S <= 0.:
EI = 0.
elif S > 0.:
else:
EI_one = ((y_min - self.predict_normalized(x)) * (0.5 + 0.5*m.erf((
1./np.sqrt(2.))*((y_min - self.predict_normalized(x)) /
S))))
Expand All @@ -223,7 +223,7 @@ def weightedexpimp(self, x, w):
y_min = np.min(self.y)
if S <= 0.:
EI = 0.
elif S > 0.:
else:
EI_one = w*((y_min - self.predict_normalized(x)) * (0.5 +
0.5*m.erf((1./np.sqrt(2.))*((y_min -
self.predict_normalized(x)) / S))))
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8 changes: 4 additions & 4 deletions pyKriging/matrixops.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,7 +6,7 @@ class matrixops():

def __init__(self):
self.LnDetPsi = None
self.Psi = np.zeros((self.n,self.n), dtype=np.float)
self.Psi = np.zeros((self.n,self.n), dtype=float)
self.psi = np.zeros((self.n,1))
self.one = np.ones(self.n)
self.mu = None
Expand All @@ -22,7 +22,7 @@ def updateData(self):
self.distance[i,j]= np.abs((self.X[i]-self.X[j]))

def updatePsi(self):
self.Psi = np.zeros((self.n,self.n), dtype=np.float)
self.Psi = np.zeros((self.n,self.n), dtype=float)
self.one = np.ones(self.n)
self.psi = np.zeros((self.n,1))
newPsi = np.exp(-np.sum(self.theta*np.power(self.distance,self.pl), axis=2))
Expand All @@ -32,7 +32,7 @@ def updatePsi(self):
self.U = self.U.T

def regupdatePsi(self):
self.Psi = np.zeros((self.n,self.n), dtype=np.float)
self.Psi = np.zeros((self.n,self.n), dtype=float)
self.one = np.ones(self.n)
self.psi = np.zeros((self.n,1))
newPsi = np.exp(-np.sum(self.theta*np.power(self.distance,self.pl), axis=2))
Expand Down Expand Up @@ -107,4 +107,4 @@ def regression_predicterr_normalized(self,x):
pass

SSqr = np.abs(SSqr[0])
return np.power(SSqr,0.5)[0]
return np.power(SSqr,0.5)[0]