def preproc_hist(self):
self.clean_pred(key=self.pred.dtype.names[0])
arrays = [self.hfeatures, self.pred]
histarray = munge.join_rec_arrays(arrays)
self.X = histarray.view((np.float, len(histarray.dtype.names)))
self.nHalos = len(self.X)
pdf, self.edges = self.histogram(self.X, normed=True)
def preproc_hist(self):
self.clean_pred(key=self.pred.dtype.names[0])
arrays = [self.hfeatures, self.pred]
histarray = munge.join_rec_arrays(arrays)
histarray = histarray.view((np.float, len(histarray.dtype.names)))
counts, edges = self.histogram(histarray, normed=True)
self.X, self.y, self.edges = self.flattenHist(counts, edges)
def preproc_hist(self):
self.clean_pred(key=self.pred.dtype.names[0])
arrays = [self.hfeatures, self.pred]
histarray = munge.join_rec_arrays(arrays)
histarray = histarray.view((np.float, len(histarray.dtype.names)))
pdf, self.edges = self.histogram(histarray, normed=True)
self.support, self.jpdf, self.edges = self.flattenHist(pdf, self.edges)
self.X = np.atleast_2d(self.hfeatures.view((float, len(self.hfeatures[0])))).T
self.y = self.make_labels()
def binedges(self):
"""
Calculate feature bin edges for feature distribution calculation
"""
arrays = [self.hfeatures, self.pred]
histarray = munge.join_rec_arrays(arrays)
histarray = histarray.view((np.float, len(histarray.dtype.names)))
counts, edges = self.histogram(histarray, normed=True)
X, y, edges = self.flattenHist(counts, edges)
self.edges = edges
def visModel(self):
f, ax = plt.subplots(2)
pred = self.predict(self.X_test)
histarray = np.vstack((self.X_test.T, pred.T)).T
counts, edges = self.histogram(histarray)
X, y, edges = self.flattenHist(counts, edges)
f, ax = self.visDensity(X, y, label='Pred')
arrays = [self.hfeatures, self.pred]
histarray = munge.join_rec_arrays(arrays)
counts, edges = self.histogram(histarray)
X, y, edges = self.flattenHist(counts, edges)
f, ax = self.visDensity(X, y, f=f, ax=ax, label='Truth')
plt.legend()
