rename omega, pi, mu, rho and kappas

ComputationalPsychiatry · Oct 2, 2023 · 04d1e7a · 04d1e7a
1 parent c876298
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diff --git a/README.md b/README.md
@@ -63,9 +63,9 @@ u, _ = load_data("binary")
 hgf = HGF(
     n_levels=2,
     model_type="binary",
-    initial_mu={"1": .0, "2": .5},
-    initial_pi={"1": .0, "2": 1e4},
-    omega={"2": -3.0},
+    initial_mean={"1": .0, "2": .5},
+    initial_precision={"1": .0, "2": 1e4},
+    tonic_volatility={"2": -3.0},
 )
 
 # add new observations

diff --git a/docs/source/index.md b/docs/source/index.md
@@ -66,9 +66,9 @@ u, _ = load_data("binary")
 hgf = HGF(
     n_levels=2,
     model_type="binary",
-    initial_mu={"1": .0, "2": .5},
-    initial_pi={"1": .0, "2": 1e4},
-    omega={"2": -3.0},
+    initial_mean={"1": .0, "2": .5},
+    initial_precision={"1": .0, "2": 1e4},
+    tonic_volatility={"2": -3.0},
 )
 
 # add new observations

diff --git a/docs/source/notebooks/1.1-Binary_HGF.ipynb b/docs/source/notebooks/1.1-Binary_HGF.ipynb
@@ -132,9 +132,9 @@
     "two_levels_hgf = HGF(\n",
     "    n_levels=2,\n",
     "    model_type=\"binary\",\n",
-    "    initial_mu={\"1\": .0, \"2\": .5},\n",
-    "    initial_pi={\"1\": .0, \"2\": 1.0},\n",
-    "    omega={\"2\": -3.0},\n",
+    "    initial_mean={\"1\": .0, \"2\": .5},\n",
+    "    initial_precision={\"1\": .0, \"2\": 1.0},\n",
+    "    tonic_volatility={\"2\": -3.0},\n",
     ")"
    ]
   },
@@ -379,11 +379,11 @@
     "three_levels_hgf = HGF(\n",
     "    n_levels=3,\n",
     "    model_type=\"binary\",\n",
-    "    initial_mu={\"1\": .0, \"2\": .5, \"3\": 0.},\n",
-    "    initial_pi={\"1\": .0, \"2\": 1.0, \"3\": 1.0},\n",
-    "    omega={\"1\": None, \"2\": -3.0, \"3\": -2.0},\n",
-    "    rho={\"1\": None, \"2\": 0.0, \"3\": 0.0},\n",
-    "    kappas={\"1\": None, \"2\": 1.0},\n",
+    "    initial_mean={\"1\": .0, \"2\": .5, \"3\": 0.},\n",
+    "    initial_precision={\"1\": .0, \"2\": 1.0, \"3\": 1.0},\n",
+    "    tonic_volatility={\"1\": None, \"2\": -3.0, \"3\": -2.0},\n",
+    "    tonic_drift={\"1\": None, \"2\": 0.0, \"3\": 0.0},\n",
+    "    volatility_coupling={\"1\": None, \"2\": 1.0},\n",
     "    eta0=0.0,\n",
     "    eta1=1.0,\n",
     "    binary_precision=jnp.inf,\n",
@@ -822,11 +822,11 @@
     "hgf_mcmc = HGF(\n",
     "    n_levels=2,\n",
     "    model_type=\"binary\",\n",
-    "    initial_mu={\"1\": jnp.inf, \"2\": 0.5},\n",
-    "    initial_pi={\"1\": 0.0, \"2\": 1.0},\n",
-    "    omega={\"1\": jnp.inf, \"2\": omega_2},\n",
-    "    rho={\"1\": 0.0, \"2\": 0.0},\n",
-    "    kappas={\"1\": 1.0}).input_data(\n",
+    "    initial_mean={\"1\": jnp.inf, \"2\": 0.5},\n",
+    "    initial_precision={\"1\": 0.0, \"2\": 1.0},\n",
+    "    tonic_volatility={\"1\": jnp.inf, \"2\": omega_2},\n",
+    "    tonic_drift={\"1\": 0.0, \"2\": 0.0},\n",
+    "    volatility_coupling={\"1\": 1.0}).input_data(\n",
     "        input_data=u\n",
     "    )"
    ]
@@ -1158,11 +1158,11 @@
     "hgf_mcmc = HGF(\n",
     "    n_levels=3,\n",
     "    model_type=\"binary\",\n",
-    "    initial_mu={\"1\": jnp.inf, \"2\": 0.5, \"3\": 0.0},\n",
-    "    initial_pi={\"1\": 0.0, \"2\": 1e4, \"3\": 1e1},\n",
-    "    omega={\"1\": jnp.inf, \"2\": omega_2, \"3\": omega_3},\n",
-    "    rho={\"1\": 0.0, \"2\": 0.0, \"3\": 0.0},\n",
-    "    kappas={\"1\": 1.0, \"2\": 1.0}).input_data(\n",
+    "    initial_mean={\"1\": jnp.inf, \"2\": 0.5, \"3\": 0.0},\n",
+    "    initial_precision={\"1\": 0.0, \"2\": 1e4, \"3\": 1e1},\n",
+    "    tonic_volatility={\"1\": jnp.inf, \"2\": omega_2, \"3\": omega_3},\n",
+    "    tonic_drift={\"1\": 0.0, \"2\": 0.0, \"3\": 0.0},\n",
+    "    volatility_coupling={\"1\": 1.0, \"2\": 1.0}).input_data(\n",
     "        input_data=u\n",
     "    )"
    ]

diff --git a/docs/source/notebooks/1.1-Binary_HGF.md b/docs/source/notebooks/1.1-Binary_HGF.md
@@ -76,9 +76,9 @@ slideshow:
 two_levels_hgf = HGF(
     n_levels=2,
     model_type="binary",
-    initial_mu={"1": .0, "2": .5},
-    initial_pi={"1": .0, "2": 1.0},
-    omega={"2": -3.0},
+    initial_mean={"1": .0, "2": .5},
+    initial_precision={"1": .0, "2": 1.0},
+    tonic_volatility={"2": -3.0},
 )
 ```
 
@@ -142,11 +142,11 @@ Here, we create a new {py:class}`pyhgf.model.HGF` instance, setting the number o
 three_levels_hgf = HGF(
     n_levels=3,
     model_type="binary",
-    initial_mu={"1": .0, "2": .5, "3": 0.},
-    initial_pi={"1": .0, "2": 1.0, "3": 1.0},
-    omega={"1": None, "2": -3.0, "3": -2.0},
-    rho={"1": None, "2": 0.0, "3": 0.0},
-    kappas={"1": None, "2": 1.0},
+    initial_mean={"1": .0, "2": .5, "3": 0.},
+    initial_precision={"1": .0, "2": 1.0, "3": 1.0},
+    tonic_volatility={"1": None, "2": -3.0, "3": -2.0},
+    tonic_drift={"1": None, "2": 0.0, "3": 0.0},
+    volatility_coupling={"1": None, "2": 1.0},
     eta0=0.0,
     eta1=1.0,
     binary_precision=jnp.inf,
@@ -244,11 +244,11 @@ omega_2 = az.summary(two_level_hgf_idata)["mean"]["omega_2"]
 hgf_mcmc = HGF(
     n_levels=2,
     model_type="binary",
-    initial_mu={"1": jnp.inf, "2": 0.5},
-    initial_pi={"1": 0.0, "2": 1.0},
-    omega={"1": jnp.inf, "2": omega_2},
-    rho={"1": 0.0, "2": 0.0},
-    kappas={"1": 1.0}).input_data(
+    initial_mean={"1": jnp.inf, "2": 0.5},
+    initial_precision={"1": 0.0, "2": 1.0},
+    tonic_volatility={"1": jnp.inf, "2": omega_2},
+    tonic_drift={"1": 0.0, "2": 0.0},
+    volatility_coupling={"1": 1.0}).input_data(
         input_data=u
     )
 ```
@@ -330,11 +330,11 @@ omega_3 = az.summary(three_level_hgf_idata)["mean"]["omega_3"]
 hgf_mcmc = HGF(
     n_levels=3,
     model_type="binary",
-    initial_mu={"1": jnp.inf, "2": 0.5, "3": 0.0},
-    initial_pi={"1": 0.0, "2": 1e4, "3": 1e1},
-    omega={"1": jnp.inf, "2": omega_2, "3": omega_3},
-    rho={"1": 0.0, "2": 0.0, "3": 0.0},
-    kappas={"1": 1.0, "2": 1.0}).input_data(
+    initial_mean={"1": jnp.inf, "2": 0.5, "3": 0.0},
+    initial_precision={"1": 0.0, "2": 1e4, "3": 1e1},
+    tonic_volatility={"1": jnp.inf, "2": omega_2, "3": omega_3},
+    tonic_drift={"1": 0.0, "2": 0.0, "3": 0.0},
+    volatility_coupling={"1": 1.0, "2": 1.0}).input_data(
         input_data=u
     )
 ```

diff --git a/docs/source/notebooks/1.2-Categorical_HGF.md b/docs/source/notebooks/1.2-Categorical_HGF.md
@@ -124,7 +124,7 @@ tags: [hide-cell]
 ---
 fig, axs = plt.subplots(nrows=5, figsize=(12, 9), sharex=True)
 plot_nodes(categorical_hgf, node_idxs=31, axs=axs[0])
-axs[1].imshow(categorical_hgf.node_trajectories[0]["mu"].T, interpolation="none", aspect="auto")
+axs[1].imshow(categorical_hgf.node_trajectories[0]["mean"].T, interpolation="none", aspect="auto")
 axs[1].set_title("Mean of the implied Dirichlet distribution", loc="left")
 axs[1].set_ylabel("Categories")
 
@@ -191,7 +191,7 @@ def categorical_surprise(omega_2, hgf, input_data):
     for va_pa in hgf.edges[0].value_parents:
         for va_pa_va_pa in hgf.edges[va_pa].value_parents:
             for va_pa_va_pa_va_pa in hgf.edges[va_pa_va_pa].value_parents:
-                hgf.attributes[va_pa_va_pa_va_pa]["omega"] = omega_2
+                hgf.attributes[va_pa_va_pa_va_pa]["tonic_volatility"] = omega_2
 
     # fit the model to new data
     hgf.input_data(input_data=input_data.T)

diff --git a/docs/source/notebooks/1.3-Continuous_HGF.ipynb b/docs/source/notebooks/1.3-Continuous_HGF.ipynb
@@ -117,9 +117,9 @@
     "two_levels_continuous_hgf = HGF(\n",
     "    n_levels=2,\n",
     "    model_type=\"continuous\",\n",
-    "    initial_mu={\"1\": timeserie[0], \"2\": 0.0},\n",
-    "    initial_pi={\"1\": 1e4, \"2\": 1e1},\n",
-    "    omega={\"1\": -13.0, \"2\": -2.0}\n",
+    "    initial_mean={\"1\": timeserie[0], \"2\": 0.0},\n",
+    "    initial_precision={\"1\": 1e4, \"2\": 1e1},\n",
+    "    tonic_volatility={\"1\": -13.0, \"2\": -2.0}\n",
     ")"
    ]
   },
@@ -365,9 +365,9 @@
     "three_levels_continuous_hgf = HGF(\n",
     "    n_levels=3,\n",
     "    model_type=\"continuous\",\n",
-    "    initial_mu={\"1\": 1.04, \"2\": 0.0, \"3\": 0.0},\n",
-    "    initial_pi={\"1\": 1e4, \"2\": 1e1, \"3\": 1e1},\n",
-    "    omega={\"1\": -13.0, \"2\": -2.0, \"3\": -2.0}\n",
+    "    initial_mean={\"1\": 1.04, \"2\": 0.0, \"3\": 0.0},\n",
+    "    initial_precision={\"1\": 1e4, \"2\": 1e1, \"3\": 1e1},\n",
+    "    tonic_volatility={\"1\": -13.0, \"2\": -2.0, \"3\": -2.0}\n",
     ")"
    ]
   },
@@ -574,9 +574,9 @@
     "three_levels_continuous_hgf_bis = HGF(\n",
     "    n_levels=3,\n",
     "    model_type=\"continuous\",\n",
-    "    initial_mu={\"1\": 1.04, \"2\": 0.0, \"3\": 0.0},\n",
-    "    initial_pi={\"1\": 1e4, \"2\": 1e1, \"3\": 1e1},\n",
-    "    omega={\"1\": -13.0, \"2\": -1.0, \"3\": -2.0},\n",
+    "    initial_mean={\"1\": 1.04, \"2\": 0.0, \"3\": 0.0},\n",
+    "    initial_precision={\"1\": 1e4, \"2\": 1e1, \"3\": 1e1},\n",
+    "    tonic_volatility={\"1\": -13.0, \"2\": -1.0, \"3\": -2.0},\n",
     ").input_data(input_data=timeserie)"
    ]
   },
@@ -938,9 +938,9 @@
     "hgf_mcmc = HGF(\n",
     "    n_levels=2,\n",
     "    model_type=\"continuous\",\n",
-    "    initial_mu={\"1\": timeserie[0], \"2\": 0.0},\n",
-    "    initial_pi={\"1\": 1e4, \"2\": 1e1},\n",
-    "    omega={\"1\": omega_1, \"2\": -2.0}).input_data(\n",
+    "    initial_mean={\"1\": timeserie[0], \"2\": 0.0},\n",
+    "    initial_precision={\"1\": 1e4, \"2\": 1e1},\n",
+    "    tonic_volatility={\"1\": omega_1, \"2\": -2.0}).input_data(\n",
     "        input_data=timeserie\n",
     "    )"
    ]
@@ -1249,9 +1249,9 @@
     "hgf_mcmc = HGF(\n",
     "    n_levels=3,\n",
     "    model_type=\"continuous\",\n",
-    "    initial_mu={\"1\": timeserie[0], \"2\": 0.0, \"3\": 0.0},\n",
-    "    initial_pi={\"1\": 1e4, \"2\": 1e1, \"3\": 1e1},\n",
-    "    omega={\"1\": omega_1, \"2\": -2.0, \"3\": -2.0}).input_data(\n",
+    "    initial_mean={\"1\": timeserie[0], \"2\": 0.0, \"3\": 0.0},\n",
+    "    initial_precision={\"1\": 1e4, \"2\": 1e1, \"3\": 1e1},\n",
+    "    tonic_volatility={\"1\": omega_1, \"2\": -2.0, \"3\": -2.0}).input_data(\n",
     "        input_data=timeserie\n",
     "    )"
    ]

diff --git a/docs/source/notebooks/1.3-Continuous_HGF.md b/docs/source/notebooks/1.3-Continuous_HGF.md
@@ -61,9 +61,9 @@ The default response function for a continuous HGF is the [sum of the Gaussian s
 two_levels_continuous_hgf = HGF(
     n_levels=2,
     model_type="continuous",
-    initial_mu={"1": timeserie[0], "2": 0.0},
-    initial_pi={"1": 1e4, "2": 1e1},
-    omega={"1": -13.0, "2": -2.0}
+    initial_mean={"1": timeserie[0], "2": 0.0},
+    initial_precision={"1": 1e4, "2": 1e1},
+    tonic_volatility={"1": -13.0, "2": -2.0}
 )
 ```
 
@@ -121,9 +121,9 @@ The three-level HGF can add a meta-volatility layer to the model. This can be us
 three_levels_continuous_hgf = HGF(
     n_levels=3,
     model_type="continuous",
-    initial_mu={"1": 1.04, "2": 0.0, "3": 0.0},
-    initial_pi={"1": 1e4, "2": 1e1, "3": 1e1},
-    omega={"1": -13.0, "2": -2.0, "3": -2.0}
+    initial_mean={"1": 1.04, "2": 0.0, "3": 0.0},
+    initial_precision={"1": 1e4, "2": 1e1, "3": 1e1},
+    tonic_volatility={"1": -13.0, "2": -2.0, "3": -2.0}
 )
 ```
 
@@ -160,9 +160,9 @@ The overall amount of surprise returned by the three-level HGF is quite similar
 three_levels_continuous_hgf_bis = HGF(
     n_levels=3,
     model_type="continuous",
-    initial_mu={"1": 1.04, "2": 0.0, "3": 0.0},
-    initial_pi={"1": 1e4, "2": 1e1, "3": 1e1},
-    omega={"1": -13.0, "2": -1.0, "3": -2.0},
+    initial_mean={"1": 1.04, "2": 0.0, "3": 0.0},
+    initial_precision={"1": 1e4, "2": 1e1, "3": 1e1},
+    tonic_volatility={"1": -13.0, "2": -1.0, "3": -2.0},
 ).input_data(input_data=timeserie)
 ```
 
@@ -261,9 +261,9 @@ omega_1 = az.summary(two_level_hgf_idata)["mean"]["omega_1"]
 hgf_mcmc = HGF(
     n_levels=2,
     model_type="continuous",
-    initial_mu={"1": timeserie[0], "2": 0.0},
-    initial_pi={"1": 1e4, "2": 1e1},
-    omega={"1": omega_1, "2": -2.0}).input_data(
+    initial_mean={"1": timeserie[0], "2": 0.0},
+    initial_precision={"1": 1e4, "2": 1e1},
+    tonic_volatility={"1": omega_1, "2": -2.0}).input_data(
         input_data=timeserie
     )
 ```
@@ -327,9 +327,9 @@ omega_1 = az.summary(three_level_hgf_idata)["mean"]["omega_1"]
 hgf_mcmc = HGF(
     n_levels=3,
     model_type="continuous",
-    initial_mu={"1": timeserie[0], "2": 0.0, "3": 0.0},
-    initial_pi={"1": 1e4, "2": 1e1, "3": 1e1},
-    omega={"1": omega_1, "2": -2.0, "3": -2.0}).input_data(
+    initial_mean={"1": timeserie[0], "2": 0.0, "3": 0.0},
+    initial_precision={"1": 1e4, "2": 1e1, "3": 1e1},
+    tonic_volatility={"1": omega_1, "2": -2.0, "3": -2.0}).input_data(
         input_data=timeserie
     )
 ```

diff --git a/index.md b/index.md
@@ -66,9 +66,9 @@ u, _ = pyhgf.updates.prediction_error.continuous
 hgf = HGF(
     n_levels=2,
     model_type="binary",
-    initial_mu={"1": .0, "2": .5},
-    initial_pi={"1": .0, "2": 1e4},
-    omega={"2": -3.0},
+    initial_mean={"1": .0, "2": .5},
+    initial_precision={"1": .0, "2": 1e4},
+    tonic_volatility={"2": -3.0},
 )
 
 # add new observations