def __init__(self):
super(ImgEnv, 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.ImageFiles = []
self.SetTags("ImageFiles", 'desc:"paths to images"')
self.Images = []
self.SetTags("Images", 'desc:"images (preload for speed)"')
self.ImageIdx = env.CurPrvInt()
self.SetTags("ImageIdx", 'desc:"current image index"')
self.Vis = Vis()
self.SetTags("Vis", 'desc:"visual processing params"')
self.XFormRand = vxform.Rand()
self.SetTags("XFormRand", 'desc:"random transform parameters"')
self.XForm = vxform.XForm()
self.SetTags("XForm", 'desc:"current -- prev transforms"')
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"')
self.OrigImg = etensor.Float32()
self.SetTags("OrigImg",
'desc:"original image prior to random transforms"')
def __init__(self):
super(ProbeEnv, 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.Words = go.Slice_string()
self.SetTags(
"Words",
'desc:"list of words used for activating state units according to index"'
)
self.WordState = etensor.Float32()
self.SetTags("WordState", 'desc:"current sentence activation state"')
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 sequence - how many steps taken within current sequence -- it resets to 0 at start of each sequence"'
)
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 __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 __init__(self):
super(LEDEnv, 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.Draw = LEDraw()
self.SetTags("Draw", 'desc:"draws LEDs onto image"')
self.Vis = Vis()
self.SetTags("Vis", 'desc:"visual processing params"')
self.MinLED = int()
self.SetTags(
"MinLED",
'min:"0" max:"19" desc:"minimum LED number to draw (0-19)"')
self.MaxLED = int()
self.SetTags(
"MaxLED",
'min:"0" max:"19" desc:"maximum LED number to draw (0-19)"')
self.CurLED = int()
self.SetTags("CurLED",
'inactive:"+" desc:"current LED number that was drawn"')
self.PrvLED = int()
self.SetTags("PrvLED",
'inactive:"+" desc:"previous LED number that was drawn"')
self.XFormRand = vxform.Rand()
self.SetTags("XFormRand", 'desc:"random transform parameters"')
self.XForm = vxform.XForm()
self.SetTags("XForm", 'desc:"current -- prev transforms"')
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"')
self.OrigImg = etensor.Float32()
self.SetTags("OrigImg",
'desc:"original image prior to random transforms"')
self.Output = etensor.Float32()
self.SetTags("Output", 'desc:"CurLED one-hot output tensor"')
def __init__(self):
super(SemEnv, 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.Sequential = False
self.SetTags(
"Sequential",
'desc:"if true, go sequentially through paragraphs -- else permuted"'
)
self.Order = go.Slice_int()
self.SetTags(
"Order",
'desc:"permuted order of paras to present if not sequential -- updated every time through the list"'
)
self.TextFiles = []
self.SetTags("TextFiles", 'desc:"paths to text files"')
self.Words = go.Slice_string()
self.SetTags("Words", 'desc:"list of words, in alpha order"')
self.WordMap = {}
self.SetTags("WordMap",
'view:"-" desc:"map of words onto index in Words list"')
self.CurParaState = etensor.Float32()
self.SetTags("CurParaState", 'desc:"current para activation state"')
self.Paras = []
self.SetTags("Paras", 'view:"-" desc:"paragraphs"')
self.ParaLabels = []
self.SetTags(
"ParaLabels",
'view:"-" desc:"special labels for each paragraph (provided in first word of para)"'
)
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 -- this is the index into Paras"'
)
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 __init__(self):
super(SentGenEnv, 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.Rules = esg.Rules()
self.SetTags(
"Rules",
'desc:"core sent-gen rules -- loaded from a grammar / rules file -- Gen() here generates one sentence"'
)
self.PPassive = float()
self.SetTags(
"PPassive",
'desc:"probability of generating passive sentence forms"')
self.WordTrans = {}
self.SetTags(
"WordTrans",
'desc:"translate unambiguous words into ambiguous words"')
self.Words = go.Slice_string()
self.SetTags(
"Words",
'desc:"list of words used for activating state units according to index"'
)
self.WordMap = {}
self.SetTags("WordMap", 'desc:"map of words onto index in Words list"')
self.Roles = []
self.SetTags(
"Roles",
'desc:"list of roles used for activating state units according to index"'
)
self.RoleMap = {}
self.SetTags("RoleMap", 'desc:"map of roles onto index in Roles list"')
self.Fillers = []
self.SetTags(
"Fillers",
'desc:"list of filler concepts used for activating state units according to index"'
)
self.FillerMap = {}
self.SetTags("FillerMap",
'desc:"map of roles onto index in Words list"')
self.AmbigVerbs = []
self.SetTags("AmbigVerbs", 'desc:"ambiguous verbs"')
self.AmbigNouns = []
self.SetTags("AmbigNouns", 'desc:"ambiguous nouns"')
self.AmbigVerbsMap = {}
self.SetTags("AmbigVerbsMap", 'desc:"map of ambiguous verbs"')
self.AmbigNounsMap = {}
self.SetTags("AmbigNounsMap", 'desc:"map of ambiguous nouns"')
self.CurSentOrig = []
self.SetTags(
"CurSentOrig",
'desc:"original current sentence as generated from Rules"')
self.CurSent = []
self.SetTags(
"CurSent",
'desc:"current sentence, potentially transformed to passive form"')
self.NAmbigNouns = int()
self.SetTags("NAmbigNouns", 'desc:"number of ambiguous nouns"')
self.NAmbigVerbs = int()
self.SetTags("NAmbigVerbs",
'desc:"number of ambiguous verbs (0 or 1)"')
self.SentInputs = []
self.SetTags(
"SentInputs",
'desc:"generated sequence of sentence inputs including role-filler queries"'
)
self.SentIdx = env.CurPrvInt()
self.SetTags("SentIdx", 'desc:"current index within sentence inputs"')
self.QType = str()
self.SetTags(
"QType",
'desc:"current question type -- from 4th value of SentInputs"')
self.WordState = etensor.Float32()
self.SetTags("WordState", 'desc:"current sentence activation state"')
self.RoleState = etensor.Float32()
self.SetTags("RoleState", 'desc:"current role query activation state"')
self.FillerState = etensor.Float32()
self.SetTags("FillerState",
'desc:"current filler query activation state"')
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.Seq = env.Ctr()
self.SetTags("Seq",
'view:"inline" desc:"sequence counter within epoch"')
self.Tick = env.Ctr()
self.SetTags("Tick",
'view:"inline" desc:"tick counter within sequence"')
self.Trial = env.Ctr()
self.SetTags(
"Trial",
'view:"inline" desc:"trial is the step counter within sequence - how many steps taken within current sequence -- it resets to 0 at start of each sequence"'
)