Merge pull request #884 from sbenthall/calcExpectation

Calc expectation

Documentation/CHANGELOG.md

@@ -8,6 +8,14 @@ For more information on HARK, see [our Github organization](https://github.com/e
 
 ## Changes
 
+### 0.10.9
+
+Release Data: TBD
+
+#### Major Changes
+
+* calcExpectations method for taking the expectation of a distribution over a function (#884)[https://github.com/econ-ark/HARK/pull/884/]
+
 ### 0.10.8
 
 Release Data: Nov. 05 2020

HARK/ConsumptionSaving/ConsAggShockModel.py

@@ -29,7 +29,12 @@
     CRRAutility_inv,
     makeGridExpMult,
 )
-from HARK.distribution import Uniform, calcExpectation
+from HARK.distribution import (
+    MeanOneLogNormal,
+    Uniform,
+    combineIndepDstns,
+    calcExpectation
+)
 from HARK.ConsumptionSaving.ConsIndShockModel import (
     ConsumerSolution,
     IndShockConsumerType,
@@ -1109,6 +1114,8 @@ def solveConsAggShock(
         The net interest factor on assets as a function of capital ratio k.
     wFunc : function
         The wage rate for labor as a function of capital-to-labor ratio k.
+    DeprFac : float
+        Capital Depreciation Rate
 
     Returns
     -------
@@ -1449,6 +1456,8 @@ def solveConsAggMarkov(
         The net interest factor on assets as a function of capital ratio k.
     wFunc : function
         The wage rate for labor as a function of capital-to-labor ratio k.
+    DeprFac : float
+        Capital Depreciation Rate
 
     Returns
     -------

HARK/distribution.py

@@ -84,6 +84,8 @@ def draw(self, N):
         ----------
         N : int
             Number of draws in each row.
+        seed : int
+            Seed for random number generator.
 
         Returns:
         ------------
@@ -217,7 +219,6 @@ def __init__(self, sigma=1.0, seed=0):
         mu = -0.5 * sigma ** 2
         super().__init__(mu=mu, sigma=sigma, seed=seed)
 
-
 class Normal(Distribution):
     """
     A Normal distribution.
@@ -584,6 +585,7 @@ def drawDiscrete(self, N, X=None, exact_match=False):
                 int
             )  # cutoff points between discrete outcomes
             top = 0
+
             # Make a list of event indices that closely matches the discrete distribution
             event_list = []
             for j in range(events.size):
@@ -964,8 +966,8 @@ def combineIndepDstns(*distributions, seed=0):
     # probability array. So get the probability array, P_out, here.
     P_out = np.prod(np.array(P_temp), axis=0)
 
-    assert np.isclose(np.sum(P_out),1),'Probabilities do not sum to 1!'
-    return DiscreteDistribution(P_out, X_out)
+    assert np.isclose(np.sum(P_out), 1), "Probabilities do not sum to 1!"
+    return DiscreteDistribution(P_out, X_out, seed=seed)
 
 
 def calcExpectation(dstn,func=None,values=None):

HARK/tests/test_distribution.py

@@ -53,7 +53,14 @@ def test_Weibull(self):
         self.assertEqual(distribution.Weibull().draw(1)[0], 0.79587450816311)
 
     def test_Uniform(self):
+        uni = distribution.Uniform()
+
         self.assertEqual(distribution.Uniform().draw(1)[0], 0.5488135039273248)
 
+        self.assertEqual(
+            distribution.calcExpectation(uni.approx(10))[0],
+            0.5
+        )
+
     def test_Bernoulli(self):
         self.assertEqual(distribution.Bernoulli().draw(1)[0], False)