def phi(self, s, a, sparse=False, format="csr"):
if sparse:
cols = np.array([0] * (self.nrbf + 1))
rows = np.array([a * self.nrbf + i for i in range(self.nrbf)] + [self.feature_cnt - 1])
data = np.hstack([self.rfunc(s),[1.0]])
sparse_features = sp_create_data(data,rows,cols,self.feature_cnt,1,format)
return sparse_features
else:
features = np.zeros(self.feature_cnt)
features[a * self.nrbf : a * self.nrbf + self.nrbf] = self.rfunc(s)
features[-1] = 1.0
return features
def phi(self, s, a, sparse=False, format="csr"):
if sparse:
cols = np.array([0] * (self.nrbf + 1))
rows = np.array([a * self.nrbf + i for i in range(self.nrbf)] + [self.feature_cnt - 1])
data = np.hstack([self.rfunc(s),[1.0]])
sparse_features = sp_create_data(data,rows,cols,self.feature_cnt,1,format)
return sparse_features
else:
features = np.zeros(self.feature_cnt)
features[a * self.nrbf : a * self.nrbf + self.nrbf] = self.rfunc(s)
features[-1] = 1.0
return features
def phi(self, s, a, sparse=False, format="csr"):
if sparse:
cols = np.array([0] * (self.nrbf + 1))
rows = np.array([a * self.nrbf + i for i in range(self.nrbf)] + [self.feature_cnt - 1])
data = np.array(self.rfunc(s) + [1.0])
sparse_features = sp_create_data(data,rows,cols,self.feature_cnt,1,format)
return sparse_features
else:
features = np.zeros(self.feature_cnt)
for i,f in enumerate(self.rfunc(s)):
features[a * self.nrbf + i] = f
features[-1] = 1.0
return features
def phi(self, s, a, sparse=False, format="csr"):
if sparse:
cols = np.array([0,0])
rows = np.array([s + (a * self.nstates), self.feature_cnt - 1])
data = np.array([1.0,1.0])
sparse_features = sp_create_data(data,rows,cols,self.feature_cnt,1,format)
return sparse_features
else:
features = np.zeros(self.feature_cnt)
features[s + (a * self.nstates)] = 1.0
features[-1] = 1.0
return features
def phi(self, s, a, sparse=False, format="csr"):
if sparse:
cols = np.array([0] * (self.nrbf + 1))
rows = np.array([a * self.nrbf + i for i in range(self.nrbf)] + [self.feature_cnt - 1])
data = np.array(self.rfunc(s) + [1.0])
sparse_features = sp_create_data(data,rows,cols,self.feature_cnt,1,format)
return sparse_features
else:
features = np.zeros(self.feature_cnt)
for i,f in enumerate(self.rfunc(s)):
features[a * self.nrbf + i] = f
features[-1] = 1.0
return features
def phi(self, s, a, sparse=False, format="csr"):
if sparse:
cols = np.array([0,0])
rows = np.array([s + (a * self.nstates), self.feature_cnt - 1])
data = np.array([1.0,1.0])
sparse_features = sp_create_data(data,rows,cols,self.feature_cnt,1,format)
return sparse_features
else:
features = np.zeros(self.feature_cnt)
features[s + (a * self.nstates)] = 1.0
features[-1] = 1.0
return features
def phi(self, s, a, sparse=False, format="csr"):
obs = self.observe(s) # 100 pca weights
if not sparse:
features = np.zeros(self.nfeatures())
features[-1] = 1.0
indx = self.tile(s,a)
features[indx] = 1.0
return features
else:
cols = np.array([0] * self.nobs + [0])
rows = np.array(self.tile(s,a) + [self.nfeatures() - 1])
data = np.array([1.0] * self.nobs + [1.0])
sparse_features = sp_create_data(data, rows, cols, self.nfeatures(),1,format)
return sparse_features
def phi(self, s, a, sparse=False, format="csr"):
obs = self.observe(s) # 100 pca weights
if not sparse:
features = np.zeros(self.nfeatures())
features[-1] = 1.0
indx = self.tile(s,a)
features[indx] = 1.0
return features
else:
cols = np.array([0] * self.nobs + [0])
rows = np.array(self.tile(s,a) + [self.nfeatures() - 1])
data = np.array([1.0] * self.nobs + [1.0])
sparse_features = sp_create_data(data, rows, cols, self.nfeatures(),1,format)
return sparse_features
