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The Cortex API provides prediction capabilities, allowing you to predict the next possible states for a given twin or aggregate.
NextStateRequest Model
Used to request state predictions:
from avenieca.api.model import NextStateRequest
nsr = NextStateRequest(
module_id="aggregate001", # Required: Module identifier
range=2, # Optional: Range parameter (default: 2)
recall=2, # Optional: Recall parameter (default: 2)
n=1, # Optional: Number of predictions (default: 1)
status=None, # Optional: Filter by status
current_state=None, # Optional: Current state ID
previous_state=None, # Optional: Previous state IDs
store_response=False, # Optional: Store response flag
store_sequence=False # Optional: Store sequence flag
)
Parameters
module_id (str) - The module identifier for predictionsrange (int) - Range parameter for prediction algorithmrecall (int) - Recall parameter for prediction algorithmn (int) - Number of predictions to returnstatus (str, optional) - Filter sequences by statuscurrent_state (int, optional) - Current state ESS IDprevious_state (list, optional) - List of previous state IDsstore_response (bool) - Whether to store the responsestore_sequence (bool) - Whether to store as a sequence
Prediction Methods
predictions()
Get predictions with states mapped back to embedding inputs:
from avenieca.api.model import NextStateRequest
nsr = NextStateRequest(
module_id="aggregate001",
recall=20,
range=20,
n=1,
status="e"
)
res, status = eca.cortex.predictions(data=nsr)
if status == 200:
print("Current State:")
for twin in res.current_state:
print(f" ESS ID: {twin.ess_id}")
print(f" Module: {twin.module_id}")
print(f" State: {twin.state}")
print("\nNext States:")
for twins_list in res.next_state:
for twin in twins_list.list:
print(f" ESS ID: {twin.ess_id}")
print(f" State: {twin.state}")
predictions_raw()
Get predictions with raw aggregate values (numeric state vectors):
from avenieca.api.model import NextStateRequest
nsr = NextStateRequest(
module_id="aggregate001",
recall=20,
range=20,
n=1,
status="e"
)
res, status = eca.cortex.predictions_raw(data=nsr)
if status == 200:
print("Current State:")
for twin in res.current_state:
print(f" ESS ID: {twin.ess_id}")
print(f" State vector: {twin.state}")
print("\nNext States:")
for twins_list in res.next_state:
for twin in twins_list.list:
print(f" State vector: {twin.state}")
Response Models
NextStateResponse
Returned by predictions() with mapped states:
@dataclass
class NextStateResponse(Base):
current_state: List[Twin] # Current state twins
next_state: List[Twins] # Predicted next states
@dataclass
class Twin(Base):
aggregate_id: int # Aggregate ID
ess_id: int # ESS database ID
module_id: str # Module identifier
state: str # State mapped to embedding input
NextStateResponseRaw
Returned by predictions_raw() with raw state vectors:
@dataclass
class NextStateResponseRaw(Base):
current_state: List[TwinRaw] # Current state twins
next_state: List[TwinsRaw] # Predicted next states
@dataclass
class TwinRaw(Base):
state: List[float] # Raw state vector
aggregate_id: int # Aggregate ID
ess_id: int # ESS database ID
module_id: str # Module identifier
Complete Example
import os
from avenieca.api.eca import ECA
from avenieca.api.model import Config, NextStateRequest
# Initialize client
config = Config(
uri="http://localhost:2580/v1",
username=os.getenv("USERNAME"),
password=os.getenv("PASSWORD")
)
eca = ECA(config)
# Create prediction request
nsr = NextStateRequest(
module_id="aggregate001",
recall=20,
range=20,
n=1,
status="e"
)
# Get predictions with mapped states
res, status = eca.cortex.predictions(data=nsr)
if status == 200:
print(f"Found {len(res.next_state)} possible next states")
for i, twins in enumerate(res.next_state):
print(f"\nPrediction {i + 1}:")
for twin in twins.list:
print(f" Module: {twin.module_id}")
print(f" State: {twin.state}")
# Get raw predictions
res_raw, status = eca.cortex.predictions_raw(data=nsr)
if status == 200:
print("\nRaw predictions:")
for twin in res_raw.current_state:
print(f"Current state vector: {twin.state}")
Use Cases
- Predictive maintenance - Predict equipment failure states
- Behavior modeling - Model user or system behavior patterns
- State transitions - Understand likely state transitions
- Anomaly detection - Identify unexpected state sequences