def __init__(self, treatment, control):
assert [type(treatment), type(control)] == [ndarray, ndarray] # numpy arrays only
yt, Xt = hsplit(treatment, [1])
yc, Xc = hsplit(control, [1])
self._nt, mt = Xt.shape
self._nc, mc = Xc.shape
self._n = self._nc + self._nt # n is number of datum across both groups
assert min(mt, mc, self._nt, self._nc) >= 1 and self._n >= 3 # data shouldn't be trivial
assert mt == mc # same number of features in treatment and control
self._m = mt # store number of features
assert unique(yt).all() in [-1,1] and unique(yc).all() in [-1,1] # labels are binary
tPlusIndex = where(yt.flatten() == 1.0)[0] # index for positive in treatment
self._ntplus = len(tPlusIndex) # number of such points (length of index)
tMinusIndex = delete(range(self._nt), tPlusIndex) # index for negative in treatment
self._ntminus = self._nt - self._ntplus # number of such points
self._Dtplus = Xt[tPlusIndex] # positive treatment datum
self._Dtminus = Xt[tMinusIndex] # negative treatment datum
cPlusIndex = where(yc.flatten() == 1.0)[0] # index for positive in control
self._ncplus = len(cPlusIndex) # number of such points (length of index)
cMinusIndex = delete(range(self._nc), cPlusIndex) # index for negative in control
self._ncminus = self._nc - self._ncplus # number of such points
self._Dcplus = Xc[cPlusIndex] # positive treatment datum
self._Dcminus = Xc[cMinusIndex] # negative treatment datum
# model parameters
self.__optimized = False # indicator for whether otpimization routine was performed
options['show_progress'] = False # supress optimization output
self.w = None # hyperplane slope
self.b1 = None # treatment group intercept
self.b2 = None # control group intercept
self.threshold = None # thresholding predictor function
print("Successfully initialized.")
评论列表
文章目录
