get ability estimation from source

app/lib/irt/models/rasch.py

@@ -0,0 +1,23 @@
+import numpy as np
+
+from girth import ability_mle
+
+class Rasch:
+
+  def __init__(self, model_params, kwargs):
+    self.model_params = model_params
+    self.b_param = kwargs['b_param']
+    self.e = 2.71828
+    self.theta = kwargs['theta']
+
+  def result(self):
+    return 0.0
+
+  @classmethod
+  def ability_estimate(self, items) -> float:
+    # we'll likely have to change this to something more robust
+    # when we get into more complex response types
+    responses = np.array([[int(item.response)] for item in items])
+    difficulty = np.array([item.b_param for item in items])
+    discrimination = np.linspace(1, 1, len(difficulty))
+    return ability_mle(responses, difficulty, discrimination)

app/lib/irt/models/three_parameter_logistic.py

@@ -16,3 +16,7 @@ class ThreeParameterLogistic:
         c = self.model_params.c_param
         return c + (1 - c) * (1 / (1 + self.e**(-a *
                                                 (self.theta - self.b_param))))
+
+    @classmethod
+    def ability_estimate(self) -> float:
+        return 0.0