def __init__(self):
super(SIREnv, self).__init__()
self.Nm = str()
self.SetTags("Nm", 'desc:"name of this environment"')
self.Dsc = str()
self.SetTags("Dsc", 'desc:"description of this environment"')
self.NStim = int(4)
self.SetTags("NStim", 'desc:"number of different stimuli that can be maintained"')
self.RewVal = float(1)
self.SetTags("RewVal", 'desc:"value for reward, based on whether model output = target"')
self.NoRewVal = float(0)
self.SetTags("NoRewVal", 'desc:"value for non-reward"')
self.Act = Actions.Store
self.SetTags("Act", 'desc:"current action"')
self.Stim = int()
self.SetTags("Stim", 'desc:"current stimulus"')
self.Maint = int()
self.SetTags("Maint", 'desc:"current stimulus being maintained"')
self.Input = etensor.Float64()
self.SetTags("Input", 'desc:"input pattern with action + stim"')
self.Output = etensor.Float64()
self.SetTags("Output", 'desc:"output pattern of what to respond"')
self.Reward = etensor.Float64()
self.SetTags("Reward", 'desc:"reward value"')
self.Run = env.Ctr()
self.SetTags("Run", 'view:"inline" desc:"current run of model as provided during Init"')
self.Epoch = env.Ctr()
self.SetTags("Epoch", 'view:"inline" desc:"number of times through Seq.Max number of sequences"')
self.Trial = env.Ctr()
self.SetTags("Trial", 'view:"inline" desc:"trial is the step counter within epoch"')
def LogTstQuiz(ss, dt):
trl = ss.TstTrlLog
epc = ss.TrainEnv.Epoch.Prv # ?
nper = 4 # number of paras per quiz question: Q, A, B, C
nt = trl.Rows
nq = int(nt / nper)
pctcor = 0.0
srow = dt.Rows
dt.SetNumRows(srow + nq + 1)
for qi in range(nq):
ri = nper * qi
qv = etensor.Float64(trl.CellTensor("Hidden", ri))
mxai = 0
mxcor = 0.0
row = srow + qi
for ai in range(nper - 1):
av = etensor.Float64(trl.CellTensor("Hidden", ri+ai+1))
cor = metric.Correlation64(qv.Values, av.Values)
if cor > mxcor:
mxai = ai
mxcor = cor
dt.SetCellTensorFloat1D("Correls", row, ai, cor)
ans = go.Slice_string(["A", "B", "C"])[mxai]
err = 1.0
if mxai == 0: # A
pctcor += 1
err = 0
# note: this shows how to use agg methods to compute summary data from another
# data table, instead of incrementing on the Sim
dt.SetCellFloat("Run", row, float(ss.TrainEnv.Run.Cur))
dt.SetCellFloat("Epoch", row, float(epc))
dt.SetCellFloat("QNo", row, float(qi))
dt.SetCellString("Resp", row, ans)
dt.SetCellFloat("Err", row, err)
pctcor /= float(nq)
row = dt.Rows - 1
dt.SetCellFloat("Run", row, float(ss.TrainEnv.Run.Cur))
dt.SetCellFloat("Epoch", row, float(epc))
dt.SetCellFloat("QNo", row, -1)
dt.SetCellString("Resp", row, "Total")
dt.SetCellFloat("Err", row, pctcor)
ss.TstQuizPctCor = pctcor
# note: essential to use Go version of update when called from another goroutine
ss.TstQuizPlot.GoUpdate()
def __init__(self):
super(CondEnv, self).__init__()
self.Nm = str()
self.SetTags("Nm", 'desc:"name of this environment"')
self.Dsc = str()
self.SetTags("Dsc", 'desc:"description of this environment"')
self.TotTime = int()
self.SetTags("TotTime", 'desc:"total time for trial"')
self.CSA = OnOff()
self.SetTags(
"CSA", 'view:"inline" desc:"Conditioned stimulus A (e.g., Tone)"')
self.CSB = OnOff()
self.SetTags(
"CSB", 'view:"inline" desc:"Conditioned stimulus B (e.g., Light)"')
self.CSC = OnOff()
self.SetTags("CSC", 'view:"inline" desc:"Conditioned stimulus C"')
self.US = OnOff()
self.SetTags("US",
'view:"inline" desc:"Unconditioned stimulus -- reward"')
self.RewVal = float()
self.SetTags("RewVal", 'desc:"value for reward"')
self.NoRewVal = float()
self.SetTags("NoRewVal", 'desc:"value for non-reward"')
self.Input = etensor.Float64()
self.SetTags("Input",
'desc:"one-hot input representation of current option"')
self.Reward = etensor.Float64()
self.SetTags("Reward", 'desc:"single reward value"')
self.Run = env.Ctr()
self.SetTags(
"Run",
'view:"inline" desc:"current run of model as provided during Init"'
)
self.Epoch = env.Ctr()
self.SetTags(
"Epoch",
'view:"inline" desc:"number of times through Seq.Max number of sequences"'
)
self.Trial = env.Ctr()
self.SetTags(
"Trial",
'view:"inline" desc:"one trial is a pass through all TotTime Events"'
)
self.Event = env.Ctr()
self.SetTags(
"Event",
'view:"inline" desc:"event is one time step within Trial -- e.g., CS turning on, etc"'
)
def LogTstEpc(ss, dt):
row = dt.Rows
dt.SetNumRows(row + 1)
trl = ss.TstTrlLog
epc = ss.TrainEnv.Epoch.Prv
wr1 = etensor.Float64(trl.CellTensor("Hidden", 0))
wr2 = etensor.Float64(trl.CellTensor("Hidden", 1))
ss.TstWords = ss.WordsLabel()
ss.TstWordsCorrel = metric.Correlation64(wr1.Values, wr2.Values)
dt.SetCellFloat("Run", row, float(ss.TrainEnv.Run.Cur))
dt.SetCellFloat("Epoch", row, float(epc))
dt.SetCellString("Words", row, ss.TstWords)
dt.SetCellFloat("TstWordsCorrel", row, ss.TstWordsCorrel)
ss.TstEpcPlot.GoUpdate()
def __init__(self):
super(BanditEnv, self).__init__()
self.Nm = str()
self.SetTags("Nm", 'desc:"name of this environment"')
self.Dsc = str()
self.SetTags("Dsc", 'desc:"description of this environment"')
self.N = int()
self.SetTags("N", 'desc:"number of different inputs"')
self.P = []
self.SetTags("P",
'desc:"no-inline" desc:"probabilities for each option"')
self.RewVal = float(1)
self.SetTags("RewVal", 'desc:"value for reward"')
self.NoRewVal = float(-1)
self.SetTags("NoRewVal", 'desc:"value for non-reward"')
self.Option = env.CurPrvInt()
self.SetTags("Option", 'desc:"bandit option current / prev"')
self.RndOpt = True
self.SetTags(
"RndOpt",
'desc:"if true, select option at random each Step -- otherwise must be set externally (e.g., by model)"'
)
self.Input = etensor.Float64()
self.SetTags("Input",
'desc:"one-hot input representation of current option"')
self.Reward = etensor.Float64()
self.SetTags("Reward", 'desc:"single reward value"')
self.Run = env.Ctr()
self.SetTags(
"Run",
'view:"inline" desc:"current run of model as provided during Init"'
)
self.Epoch = env.Ctr()
self.SetTags(
"Epoch",
'view:"inline" desc:"number of times through Seq.Max number of sequences"'
)
self.Trial = env.Ctr()
self.SetTags(
"Trial",
'view:"inline" desc:"trial is the step counter within epoch"')
def UniquePatStat(ss, dt):
"""
UniquePatStat analyzes the hidden activity patterns for the single-line test inputs
to determine how many such lines have a distinct hidden pattern, as computed
from the similarity matrix across patterns
"""
hc = etensor.Float64(dt.ColByName("Hidden"))
norm.Binarize64(hc.Values, .5, 1, 0)
ix = etable.NewIdxView(dt)
ss.SimMat.TableColStd(ix, "Hidden", "TrialName", False, metric.SumSquares)
dm = ss.SimMat.Mat
nrow = dm.Dim(0)
uniq = 0
for row in range(nrow):
tsr = etensor.Float64(dm.SubSpace(go.Slice_int([row])))
nzero = 0
for vl in tsr.Values:
if vl == 0:
nzero += 1
if nzero == 1:
uniq += 1
return float(uniq)
def etensor_to_numpy(et):
"""
returns a numpy ndarray constructed from the given etensor.Tensor.
data is copied into the numpy ndarray -- it is not a view.
"""
nar = 0
if et.DataType() == etensor.UINT8:
nar = np.array(etensor.Uint8(et).Values, dtype=np.uint8)
elif et.DataType() == etensor.INT8:
nar = np.array(etensor.Int8(et).Values, dtype=np.int8)
elif et.DataType() == etensor.UINT16:
nar = np.array(etensor.Uint16(et).Values, dtype=np.uint16)
elif et.DataType() == etensor.INT16:
nar = np.array(etensor.Int16(et).Values, dtype=np.int16)
elif et.DataType() == etensor.UINT32:
nar = np.array(etensor.Uint32(et).Values, dtype=np.uint32)
elif et.DataType() == etensor.INT32:
nar = np.array(etensor.Int32(et).Values, dtype=np.int32)
elif et.DataType() == etensor.UINT64:
nar = np.array(etensor.Uint64(et).Values, dtype=np.uint64)
elif et.DataType() == etensor.INT64:
nar = np.array(etensor.Int64(et).Values, dtype=np.int64)
elif et.DataType() == etensor.FLOAT32:
nar = np.array(etensor.Float32(et).Values, dtype=np.float32)
elif et.DataType() == etensor.FLOAT64:
nar = np.array(etensor.Float64(et).Values, dtype=np.float64)
elif et.DataType() == etensor.STRING:
nar = np.array(etensor.String(et).Values)
elif et.DataType() == etensor.INT:
nar = np.array(etensor.Int(et).Values, dtype=np.intc)
elif et.DataType() == etensor.BOOL:
etb = etensor.Bits(et)
sz = etb.Len()
nar = np.zeros(sz, dtype=np.bool_)
for i in range(sz):
nar[i] = etb.Value1D(i)
else:
raise TypeError("tensor with type %s cannot be converted" %
(et.DataType().String()))
return 0
# there does not appear to be a way to set the shape at the same time as initializing
return nar.reshape(et.Shapes())
def copy_numpy_to_etensor(et, nar):
"""
copies data from numpy ndarray (nar, source) to existing etensor.Tensor (et, dest)
"""
narf = np.reshape(nar, -1)
etv = et
if et.DataType() == etensor.UINT8:
etv = etensor.Uint8(et).Values
elif et.DataType() == etensor.INT8:
etv = etensor.Int8(et).Values
elif et.DataType() == etensor.UINT16:
etv = etensor.Uint16(et).Values
elif et.DataType() == etensor.INT16:
etv = etensor.Int16(et).Values
elif et.DataType() == etensor.UINT32:
etv = etensor.Uint32(et).Values
elif et.DataType() == etensor.INT32:
etv = etensor.Int32(et).Values
elif et.DataType() == etensor.UINT64:
etv = etensor.Uint64(et).Values
elif et.DataType() == etensor.INT64:
etv = etensor.Int64(et).Values
elif et.DataType() == etensor.FLOAT32:
etv = etensor.Float32(et).Values
elif et.DataType() == etensor.FLOAT64:
etv = etensor.Float64(et).Values
elif et.DataType() == etensor.STRING:
etv = etensor.String(et).Values
elif et.DataType() == etensor.INT:
etv = etensor.Int(et).Values
elif et.DataType() == etensor.BOOL:
etb = etensor.Bits(et)
sz = min(etb.Len(), len(narf))
for i in range(sz):
narf[i] = etb.Value1D(i)
return
else:
raise TypeError("tensor with type %s cannot be copied" %
(et.DataType().String()))
return 0
etv.copy(narf) # go slice copy, not python copy = clone
