def __init__(self,
X,
y,
testfunction=None,
name='',
testPoints=None,
**kwargs):
self.X = copy.deepcopy(X)
self.y = copy.deepcopy(y)
self.testfunction = testfunction
self.name = name
self.n = self.X.shape[0]
self.k = self.X.shape[1]
self.theta = np.ones(self.k)
self.pl = np.ones(self.k) * 2.
self.sigma = 0
self.normRange = []
self.ynormRange = []
self.normalizeData()
self.sp = samplingplan.samplingplan(self.k)
self.updateData()
self.updateModel()
self.thetamin = 1e-5
self.thetamax = 100
self.pmin = 1
self.pmax = 2
# Setup functions for tracking history
self.history = {}
self.history['points'] = []
self.history['neglnlike'] = []
self.history['theta'] = []
self.history['p'] = []
self.history['rsquared'] = [0]
self.history['adjrsquared'] = [0]
self.history['chisquared'] = [1000]
self.history['lastPredictedPoints'] = []
self.history['avgMSE'] = []
if testPoints:
self.history['pointData'] = []
self.testPoints = self.sp.rlh(testPoints)
for point in self.testPoints:
testPrimitive = {}
testPrimitive['point'] = point
if self.testfunction:
testPrimitive['actual'] = self.testfunction(point)[0]
else:
testPrimitive['actual'] = None
testPrimitive['predicted'] = []
testPrimitive['mse'] = []
testPrimitive['gradient'] = []
self.history['pointData'].append(testPrimitive)
else:
self.history['pointData'] = None
matrixops.__init__(self)
def __init__(self, X, y, testfunction=None, name='', testPoints=None, **kwargs):
self.X = copy.deepcopy(X)
self.y = copy.deepcopy(y)
self.testfunction = testfunction
self.name = name
self.n = self.X.shape[0]
self.k = self.X.shape[1]
self.theta = np.ones(self.k)
self.pl = np.ones(self.k) * 2.
self.sigma = 0
self.normRange = []
self.ynormRange = []
self.normalizeData()
self.sp = samplingplan.samplingplan(self.k)
self.updateData()
self.updateModel()
self.thetamin = 1e-5
self.thetamax = 100
self.pmin = 1
self.pmax = 2
# Setup functions for tracking history
self.history = {}
self.history['points'] = []
self.history['neglnlike'] = []
self.history['theta'] = []
self.history['p'] = []
self.history['rsquared'] = [0]
self.history['adjrsquared'] = [0]
self.history['chisquared'] = [1000]
self.history['lastPredictedPoints'] = []
self.history['avgMSE'] = []
if testPoints:
self.history['pointData'] = []
self.testPoints = self.sp.rlh(testPoints)
for point in self.testPoints:
testPrimitive = {}
testPrimitive['point'] = point
if self.testfunction:
testPrimitive['actual'] = self.testfunction(point)[0]
else:
testPrimitive['actual'] = None
testPrimitive['predicted'] = []
testPrimitive['mse'] = []
testPrimitive['gradient'] = []
self.history['pointData'].append(testPrimitive)
else:
self.history['pointData'] = None
matrixops.__init__(self)
def __init__(self, X, y, testfunction=None, name='', **kwargs):
self.X = copy.deepcopy(X)
self.y = copy.deepcopy(y)
self.testfunction = testfunction
self.name = name
self.n = self.X.shape[0]
self.k = self.X.shape[1]
self.theta = np.ones(self.k)
self.pl = np.ones(self.k)
self.sigma = 0
self.normRange = []
self.ynormRange = []
self.normalizeData()
self.Lambda = 1
matrixops.__init__(self)