From 7537c7e5b7978e826f46f858d2b9169b4abf1987 Mon Sep 17 00:00:00 2001
From: ltsaprounis <leonidas.tsap@gmail.com>
Date: Wed, 20 Dec 2023 19:13:15 +0000
Subject: [PATCH 1/6] ruff for linting and formatting and CI  with linting step

---
 .github/workflows/python_ci.yml | 16 +++++++
 python/pyproject.toml           | 76 ++++++++++++++++++++++++++++++---
 2 files changed, 85 insertions(+), 7 deletions(-)
 create mode 100644 .github/workflows/python_ci.yml

diff --git a/.github/workflows/python_ci.yml b/.github/workflows/python_ci.yml
new file mode 100644
index 00000000..51350d38
--- /dev/null
+++ b/.github/workflows/python_ci.yml
@@ -0,0 +1,16 @@
+name: Python CI
+
+on:
+    push:
+        branches: [Python]
+    pull_request:
+        branches: [Python]
+
+jobs:
+    linting:
+        runs-on: ubuntu-latest
+        steps:
+            - uses: actions/checkout@v4
+            - uses: chartboost/ruff-action@v1
+              with:
+                src: "./python"
diff --git a/python/pyproject.toml b/python/pyproject.toml
index 0534d7d0..ba20aaf9 100644
--- a/python/pyproject.toml
+++ b/python/pyproject.toml
@@ -10,14 +10,8 @@ readme = "README.md"
 requires-python = ">=3.8,<3.11"
 dependencies = ["pybind11[global]>=2.6.0", "numpy>=1.14"]
 
-# [tool.setuptools.packages]
-# find = {}
-
 [project.optional-dependencies]
-dev = ["flake8", "black", "pytest", "pydocstyle", "pre-commit", "ipykernel"]
-
-# [tool.pytest.ini_options]
-# addopts = "--ignore=../src/carma"
+dev = ["pytest", "ruff", "pre-commit", "ipykernel"]
 
 [tool.scikit-build]
 # wheel.expand-macos-universal-tags = true
@@ -28,3 +22,71 @@ ninja.make-fallback = true
 cmake.verbose = true
 ninja.minimum-version = "1.5"
 cmake.minimum-version = "3.25"
+
+[tool.ruff]
+# Exclude a variety of commonly ignored directories.
+exclude = [
+    ".bzr",
+    ".direnv",
+    ".eggs",
+    ".git",
+    ".git-rewrite",
+    ".hg",
+    ".mypy_cache",
+    ".nox",
+    ".pants.d",
+    ".pytype",
+    ".ruff_cache",
+    ".svn",
+    ".tox",
+    ".venv",
+    "__pypackages__",
+    "_build",
+    "buck-out",
+    "build",
+    "dist",
+    "node_modules",
+    "venv",
+    "tools/cookiecutter_templates",
+]
+
+# Same as Black.
+line-length = 88
+indent-width = 4
+
+# Assume Python 3.8
+target-version = "py38"
+
+[tool.ruff.lint]
+# Enable Pyflakes (`F`) and a subset of the pycodestyle (`E`)  codes by default.
+# Unlike Flake8, Ruff doesn't enable pycodestyle warnings (`W`) or
+# McCabe complexity (`C901`) by default.
+select = [
+    "E4",
+    "E7",
+    "E9",
+    "F",
+    "I",    # isort rules
+    "E501",
+]
+ignore = []
+
+# Allow fix for all enabled rules (when `--fix`) is provided.
+fixable = ["ALL"]
+unfixable = []
+
+# Allow unused variables when underscore-prefixed.
+dummy-variable-rgx = "^(_+|(_+[a-zA-Z0-9_]*[a-zA-Z0-9]+?))$"
+
+[tool.ruff.format]
+# Like Black, use double quotes for strings.
+quote-style = "double"
+
+# Like Black, indent with spaces, rather than tabs.
+indent-style = "space"
+
+# Like Black, respect magic trailing commas.
+skip-magic-trailing-comma = false
+
+# Like Black, automatically detect the appropriate line ending.
+line-ending = "auto"

From 18873e96324656165054b33ffe85784a8a9b8b3e Mon Sep 17 00:00:00 2001
From: ltsaprounis <leonidas.tsap@gmail.com>
Date: Wed, 20 Dec 2023 21:03:18 +0000
Subject: [PATCH 2/6] remove dummy my_linalg module

---
 python/smooth/my_linalg/__init__.py           |  6 ----
 python/smooth/my_linalg/tests/__init__.py     |  0
 .../smooth/my_linalg/tests/test_my_linalg.py  | 33 -------------------
 3 files changed, 39 deletions(-)
 delete mode 100644 python/smooth/my_linalg/__init__.py
 delete mode 100644 python/smooth/my_linalg/tests/__init__.py
 delete mode 100644 python/smooth/my_linalg/tests/test_my_linalg.py

diff --git a/python/smooth/my_linalg/__init__.py b/python/smooth/my_linalg/__init__.py
deleted file mode 100644
index e7e7ac31..00000000
--- a/python/smooth/my_linalg/__init__.py
+++ /dev/null
@@ -1,6 +0,0 @@
-"""Example python module for using carma and pybind11."""
-
-from ._my_linalg import example_attr, add, dot_product, arma_dot_product, array_sum
-
-
-__all__ = ["example_attr", "add", "dot_product", "arma_dot_product", "array_sum"]
diff --git a/python/smooth/my_linalg/tests/__init__.py b/python/smooth/my_linalg/tests/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/python/smooth/my_linalg/tests/test_my_linalg.py b/python/smooth/my_linalg/tests/test_my_linalg.py
deleted file mode 100644
index 02db4436..00000000
--- a/python/smooth/my_linalg/tests/test_my_linalg.py
+++ /dev/null
@@ -1,33 +0,0 @@
-"""Tests the my_linalg library."""
-import numpy as np
-
-from smooth.my_linalg import add, dot_product, array_sum, arma_dot_product
-
-arr1 = np.array([1, 2])
-arr2 = np.array([3, 4])
-
-
-def test_add():
-    """Test the add function."""
-    assert add(1, 2) == 3
-
-
-def test_dot_product():
-    """Test the dot_product function"""
-    arr1 = np.array([1, 2])
-    arr2 = np.array([3, 4])
-    assert dot_product(arr1, arr2) == 11
-
-
-def test_arma_dot_product():
-    """Tests the arma_dot_product function"""
-    arr1 = np.array([1, 2])
-    arr2 = np.array([3, 4])
-    assert arma_dot_product(arr1, arr2) == 11
-
-
-def test_array_sum():
-    """Tests the array_sum function"""
-    arr1 = np.array([1, 2])
-    arr2 = np.array([3, 4])
-    assert np.all(array_sum(arr1, arr2) == np.array([4, 6]))

From d96603bbdfe8960597ece59087e031d49f614004 Mon Sep 17 00:00:00 2001
From: ltsaprounis <leonidas.tsap@gmail.com>
Date: Wed, 20 Dec 2023 21:04:05 +0000
Subject: [PATCH 3/6] pass all ruff checks (not full scope of rules)

---
 python/smooth/adam_general/adam_profile.py | 17 +++++++++++------
 python/smooth/adam_general/sma.py          |  9 ++++-----
 python/smooth/adam_general/test_script.py  |  1 +
 3 files changed, 16 insertions(+), 11 deletions(-)

diff --git a/python/smooth/adam_general/adam_profile.py b/python/smooth/adam_general/adam_profile.py
index 81f37a93..ef40340c 100644
--- a/python/smooth/adam_general/adam_profile.py
+++ b/python/smooth/adam_general/adam_profile.py
@@ -14,7 +14,8 @@ def adamProfileCreator(
     yIndex (list): The indices needed to get the specific dates (optional).
     yClasses (list): The class used for the actual data (optional).
     Returns:
-    dict: A dictionary with 'recent' (profilesRecentTable) and 'lookup' (indexLookupTable) as keys.
+    dict: A dictionary with 'recent' (profilesRecentTable) and 'lookup'
+    (indexLookupTable) as keys.
     """
     # Initialize matrices
     profilesRecentTable = np.zeros((len(lagsModelAll), lagsModelMax))
@@ -31,10 +32,12 @@ def adamProfileCreator(
         # For every row, fill the first 'lag' elements from 1 to lag
         profilesRecentTable[i, : lag[0]] = np.arange(1, lag[0] + 1)
 
-        # For the i-th row in indexLookupTable, fill with a repeated sequence starting from lagsModelMax to the end of the row.
-        # The repeated sequence is the i-th row of profileIndices, repeated enough times to cover 'obsAll' observations.
+        # For the i-th row in indexLookupTable, fill with a repeated sequence starting
+        # from lagsModelMax to the end of the row.
+        # The repeated sequence is the i-th row of profileIndices, repeated enough times
+        # to cover 'obsAll' observations.
         # '- 1' at the end adjusts these values to Python's zero-based indexing.
-        indexLookupTable[i, lagsModelMax : (lagsModelMax + obsAll)] = (  # noqa
+        indexLookupTable[i, lagsModelMax : (lagsModelMax + obsAll)] = (
             np.tile(
                 profileIndices[i, : lagsModelAll[i][0]],
                 int(np.ceil(obsAll / lagsModelAll[i][0])),
@@ -42,13 +45,15 @@ def adamProfileCreator(
             - 1
         )
 
-        # Extract unique values from from lagsModelMax to lagsModelMax + obsAll of indexLookupTable
+        # Extract unique values from from lagsModelMax to lagsModelMax + obsAll of
+        # indexLookupTable
         unique_values = np.unique(
             indexLookupTable[i, lagsModelMax : lagsModelMax + obsAll]  # noqa
         )
 
         # fix the head of teh data before the sample starts
-        # Repeat the unique values lagsModelMax times and then trim the sequence to only keep the first lagsModelMax elements
+        # Repeat the unique values lagsModelMax times and then trim the sequence to only
+        # keep the first lagsModelMax elements
         indexLookupTable[i, :lagsModelMax] = np.tile(unique_values, lagsModelMax)[
             -lagsModelMax:
         ]
diff --git a/python/smooth/adam_general/sma.py b/python/smooth/adam_general/sma.py
index c37d0c87..7ac42342 100644
--- a/python/smooth/adam_general/sma.py
+++ b/python/smooth/adam_general/sma.py
@@ -1,4 +1,5 @@
 import numpy as np
+
 from smooth.adam_general._adam_general import adam_fitter
 from smooth.adam_general.adam_profile import adamProfileCreator
 
@@ -7,7 +8,7 @@ def sma(y, order=1, h=10, holdout=False):
     """SMA"""
     y = y.astype(np.float64)
 
-    ic = lambda e: np.sum(e**2)
+    # ic = lambda e: np.sum(e**2)
     obs_all = len(y) + h * (1 - holdout)
     obs_in_sample = len(y) - h * holdout
     y_in_sample = y
@@ -25,14 +26,12 @@ def sma(y, order=1, h=10, holdout=False):
     def creator_sma(order):
         # lags_model_all = np.ones(shape=(order, 1))
         # This needs to be a vector of values
-        lags_model_all = np.arange(1, order+1, dtype="int32").reshape(order, 1)
+        lags_model_all = np.arange(1, order + 1, dtype="int32").reshape(order, 1)
         lags_model_max = int(max(lags_model_all))
         obs_states = obs_in_sample + lags_model_max
 
         profiles_recent_table, index_lookup_table = adamProfileCreator(
-            lagsModelAll=lags_model_all,
-            lagsModelMax=lags_model_max,
-            obsAll=obs_all
+            lagsModelAll=lags_model_all, lagsModelMax=lags_model_max, obsAll=obs_all
         ).values()
 
         # # This needs to be generated by a profileCreator() function
diff --git a/python/smooth/adam_general/test_script.py b/python/smooth/adam_general/test_script.py
index 535516c5..7e1e28cd 100644
--- a/python/smooth/adam_general/test_script.py
+++ b/python/smooth/adam_general/test_script.py
@@ -1,4 +1,5 @@
 import numpy as np
+
 from smooth.adam_general.sma import sma
 
 if __name__ == "__main__":

From 88e9b10f48f421280647d9ec2d5e6f6186228981 Mon Sep 17 00:00:00 2001
From: ltsaprounis <leonidas.tsap@gmail.com>
Date: Wed, 20 Dec 2023 21:34:14 +0000
Subject: [PATCH 4/6] added N rules in ruff rule scope.

---
 python/pyproject.toml                      |  9 +++--
 python/smooth/adam_general/adam_profile.py | 38 +++++++++++-----------
 python/smooth/adam_general/sma.py          | 24 +++++++-------
 3 files changed, 39 insertions(+), 32 deletions(-)

diff --git a/python/pyproject.toml b/python/pyproject.toml
index ba20aaf9..f32773be 100644
--- a/python/pyproject.toml
+++ b/python/pyproject.toml
@@ -65,11 +65,16 @@ select = [
     "E4",
     "E7",
     "E9",
+    "E501",
     "F",
     "I",    # isort rules
-    "E501",
+    "N",    # PEP-8 naming rules 
+]
+ignore = [
+    # uppercase names are used for matrices
+    "N803", # allow uppercase argument names 
+    "N806", # allow uppercase variable names
 ]
-ignore = []
 
 # Allow fix for all enabled rules (when `--fix`) is provided.
 fixable = ["ALL"]
diff --git a/python/smooth/adam_general/adam_profile.py b/python/smooth/adam_general/adam_profile.py
index ef40340c..dbadf8e6 100644
--- a/python/smooth/adam_general/adam_profile.py
+++ b/python/smooth/adam_general/adam_profile.py
@@ -1,8 +1,8 @@
 import numpy as np
 
 
-def adamProfileCreator(
-    lagsModelAll, lagsModelMax, obsAll, lags=None, yIndex=None, yClasses=None
+def adam_profile_creator(
+    lags_model_all, lags_model_max, obs_all, lags=None, y_index=None, y_classes=None
 ):
     """
     Creates recent profile and the lookup table for adam.
@@ -18,51 +18,51 @@ def adamProfileCreator(
     (indexLookupTable) as keys.
     """
     # Initialize matrices
-    profilesRecentTable = np.zeros((len(lagsModelAll), lagsModelMax))
-    indexLookupTable = np.ones((len(lagsModelAll), obsAll + lagsModelMax))
-    profileIndices = (
-        np.arange(1, lagsModelMax * len(lagsModelAll) + 1)
-        .reshape(-1, len(lagsModelAll))
+    profiles_recent_table = np.zeros((len(lags_model_all), lags_model_max))
+    index_lookup_table = np.ones((len(lags_model_all), obs_all + lags_model_max))
+    profile_indices = (
+        np.arange(1, lags_model_max * len(lags_model_all) + 1)
+        .reshape(-1, len(lags_model_all))
         .T
     )
 
     # Update matrices based on lagsModelAll
-    for i, lag in enumerate(lagsModelAll):
+    for i, lag in enumerate(lags_model_all):
         # Create the matrix with profiles based on the provided lags.
         # For every row, fill the first 'lag' elements from 1 to lag
-        profilesRecentTable[i, : lag[0]] = np.arange(1, lag[0] + 1)
+        profiles_recent_table[i, : lag[0]] = np.arange(1, lag[0] + 1)
 
         # For the i-th row in indexLookupTable, fill with a repeated sequence starting
         # from lagsModelMax to the end of the row.
         # The repeated sequence is the i-th row of profileIndices, repeated enough times
         # to cover 'obsAll' observations.
         # '- 1' at the end adjusts these values to Python's zero-based indexing.
-        indexLookupTable[i, lagsModelMax : (lagsModelMax + obsAll)] = (
+        index_lookup_table[i, lags_model_max : (lags_model_max + obs_all)] = (
             np.tile(
-                profileIndices[i, : lagsModelAll[i][0]],
-                int(np.ceil(obsAll / lagsModelAll[i][0])),
-            )[0:obsAll]
+                profile_indices[i, : lags_model_all[i][0]],
+                int(np.ceil(obs_all / lags_model_all[i][0])),
+            )[0:obs_all]
             - 1
         )
 
         # Extract unique values from from lagsModelMax to lagsModelMax + obsAll of
         # indexLookupTable
         unique_values = np.unique(
-            indexLookupTable[i, lagsModelMax : lagsModelMax + obsAll]  # noqa
+            index_lookup_table[i, lags_model_max : lags_model_max + obs_all]  # noqa
         )
 
         # fix the head of teh data before the sample starts
         # Repeat the unique values lagsModelMax times and then trim the sequence to only
         # keep the first lagsModelMax elements
-        indexLookupTable[i, :lagsModelMax] = np.tile(unique_values, lagsModelMax)[
-            -lagsModelMax:
+        index_lookup_table[i, :lags_model_max] = np.tile(unique_values, lags_model_max)[
+            -lags_model_max:
         ]
 
     # Convert to int!
-    indexLookupTable = indexLookupTable.astype(int)
+    index_lookup_table = index_lookup_table.astype(int)
 
     # Note: I skip andling of special cases (e.g., daylight saving time, leap years)
     return {
-        "recent": np.array(profilesRecentTable, dtype="float64"),
-        "lookup": np.array(indexLookupTable, dtype="int64"),
+        "recent": np.array(profiles_recent_table, dtype="float64"),
+        "lookup": np.array(index_lookup_table, dtype="int64"),
     }
diff --git a/python/smooth/adam_general/sma.py b/python/smooth/adam_general/sma.py
index 7ac42342..9c44de47 100644
--- a/python/smooth/adam_general/sma.py
+++ b/python/smooth/adam_general/sma.py
@@ -1,7 +1,7 @@
 import numpy as np
 
 from smooth.adam_general._adam_general import adam_fitter
-from smooth.adam_general.adam_profile import adamProfileCreator
+from smooth.adam_general.adam_profile import adam_profile_creator
 
 
 def sma(y, order=1, h=10, holdout=False):
@@ -30,8 +30,10 @@ def creator_sma(order):
         lags_model_max = int(max(lags_model_all))
         obs_states = obs_in_sample + lags_model_max
 
-        profiles_recent_table, index_lookup_table = adamProfileCreator(
-            lagsModelAll=lags_model_all, lagsModelMax=lags_model_max, obsAll=obs_all
+        profiles_recent_table, index_lookup_table = adam_profile_creator(
+            lags_model_all=lags_model_all,
+            lags_model_max=lags_model_max,
+            obs_all=obs_all,
         ).values()
 
         # # This needs to be generated by a profileCreator() function
@@ -44,19 +46,19 @@ def creator_sma(order):
         #     np.arange(order), (obs_all + lags_model_max, 1)
         # ).T
 
-        matF = np.ones((order, order)) / order
-        matWt = np.ones((obs_in_sample, order))
+        mat_F = np.ones((order, order)) / order
+        mat_Wt = np.ones((obs_in_sample, order))
 
-        vecG = np.ones(order) / order
+        vec_G = np.ones(order) / order
         # matVt = np.zeros((order, obs_states))
-        matVt = np.empty((order, obs_states))
+        mat_Vt = np.empty((order, obs_states))
         # matVt.fill(np.nan)
 
         adam_fitted = adam_fitter(
-            matrixVt=matVt,
-            matrixWt=matWt,
-            matrixF=matF,
-            vectorG=vecG,
+            matrixVt=mat_Vt,
+            matrixWt=mat_Wt,
+            matrixF=mat_F,
+            vectorG=vec_G,
             lags=lags_model_all,
             indexLookupTable=index_lookup_table,
             profilesRecent=profiles_recent_table,

From d1641dc6be327037e72aaa4b3b5e3a6bb536e338 Mon Sep 17 00:00:00 2001
From: ltsaprounis <leonidas.tsap@gmail.com>
Date: Mon, 8 Jan 2024 22:34:47 +0000
Subject: [PATCH 5/6] small comment in pyproject.toml

---
 python/pyproject.toml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/python/pyproject.toml b/python/pyproject.toml
index f32773be..9cb90808 100644
--- a/python/pyproject.toml
+++ b/python/pyproject.toml
@@ -61,6 +61,7 @@ target-version = "py38"
 # Enable Pyflakes (`F`) and a subset of the pycodestyle (`E`)  codes by default.
 # Unlike Flake8, Ruff doesn't enable pycodestyle warnings (`W`) or
 # McCabe complexity (`C901`) by default.
+# Rules for ruff are found here: https://docs.astral.sh/ruff/rules/
 select = [
     "E4",
     "E7",

From c0b48b2f9d69a4eb4731e90712eaa2fd4f0a400b Mon Sep 17 00:00:00 2001
From: ltsaprounis <leonidas.tsap@gmail.com>
Date: Tue, 13 Feb 2024 18:07:27 +0000
Subject: [PATCH 6/6] transfer adam_forecaster

---
 python/smooth/adam_general/sma.py         | 52 +++++++++++++++++++++--
 python/smooth/adam_general/test_script.py |  2 +-
 src/python_examples/adamGeneral.cpp       | 43 +++++++++++++++++++
 3 files changed, 93 insertions(+), 4 deletions(-)

diff --git a/python/smooth/adam_general/sma.py b/python/smooth/adam_general/sma.py
index 9c44de47..7c9fd979 100644
--- a/python/smooth/adam_general/sma.py
+++ b/python/smooth/adam_general/sma.py
@@ -1,6 +1,6 @@
 import numpy as np
 
-from smooth.adam_general._adam_general import adam_fitter
+from smooth.adam_general._adam_general import adam_fitter, adam_forecaster
 from smooth.adam_general.adam_profile import adam_profile_creator
 
 
@@ -75,6 +75,52 @@ def creator_sma(order):
             backcast=True,
         )
 
-        return adam_fitted
+        fitted_args = dict(
+            matrixVt=mat_Vt,
+            matrixWt=mat_Wt,
+            matrixF=mat_F,
+            vectorG=vec_G,
+            lags=lags_model_all,
+            indexLookupTable=index_lookup_table,
+            profilesRecent=profiles_recent_table,
+            E=E_type,
+            T=T_type,
+            S=S_type,
+            nNonSeasonal=components_num_ETS,
+            nSeasonal=components_num_ETS_seasonal,
+            nArima=order,
+            nXreg=xreg_number,
+            constant=constant_required,
+            vectorYt=y_in_sample,
+            vectorOt=ot,
+            backcast=True,
+        )
+
+        return adam_fitted, fitted_args
+
+    sma_fitted, fitted_args = creator_sma(order=order)
+
+    # need to convert some inputs to the expected dtypes. This is a temporary fix.
+    fitted_args["lags"] = np.array(fitted_args["lags"], dtype="uint64")
+    fitted_args["indexLookupTable"] = np.array(
+        fitted_args["indexLookupTable"], dtype="uint64"
+    )
+
+    sma_forecast = adam_forecaster(
+        matrixWt=fitted_args["matrixWt"],
+        matrixF=fitted_args["matrixF"],
+        lags=fitted_args["lags"],
+        indexLookupTable=fitted_args["indexLookupTable"],
+        profilesRecent=sma_fitted["profile"],
+        E=fitted_args["E"],
+        T=fitted_args["T"],
+        S=fitted_args["S"],
+        nNonSeasonal=fitted_args["nNonSeasonal"],
+        nSeasonal=fitted_args["nSeasonal"],
+        nArima=fitted_args["nArima"],
+        nXreg=fitted_args["nXreg"],
+        constant=fitted_args["constant"],
+        horizon=h,
+    )
 
-    return creator_sma(order=order)
+    return sma_forecast
diff --git a/python/smooth/adam_general/test_script.py b/python/smooth/adam_general/test_script.py
index 7e1e28cd..48bcaa58 100644
--- a/python/smooth/adam_general/test_script.py
+++ b/python/smooth/adam_general/test_script.py
@@ -5,4 +5,4 @@
 if __name__ == "__main__":
     y = np.arange(0, 100)
     results = sma(y, order=5)
-    print(results["yFitted"])
+    print(results)
diff --git a/src/python_examples/adamGeneral.cpp b/src/python_examples/adamGeneral.cpp
index 80d24514..f7569164 100644
--- a/src/python_examples/adamGeneral.cpp
+++ b/src/python_examples/adamGeneral.cpp
@@ -159,6 +159,34 @@ py::dict adamFitter(arma::mat &matrixVt, arma::mat const &matrixWt, arma::mat &m
     return result;
 }
 
+/* # Function produces the point forecasts for the specified model */
+arma::vec adamForecaster(arma::mat const &matrixWt, arma::mat const &matrixF,
+                         arma::uvec lags, arma::umat const &indexLookupTable, arma::mat profilesRecent,
+                         char const &E, char const &T, char const &S,
+                         unsigned int const &nNonSeasonal, unsigned int const &nSeasonal,
+                         unsigned int const &nArima, unsigned int const &nXreg, bool const &constant,
+                         unsigned int const &horizon)
+{
+    // unsigned int lagslength = lags.n_rows;
+    unsigned int nETS = nNonSeasonal + nSeasonal;
+    unsigned int nComponents = indexLookupTable.n_rows;
+
+    arma::vec vecYfor(horizon, arma::fill::zeros);
+
+    /* # Fill in the new xt matrix using F. Do the forecasts. */
+    for (unsigned int i = 0; i < horizon; i = i + 1)
+    {
+        vecYfor.row(i) = adamWvalue(profilesRecent(indexLookupTable.col(i)), matrixWt.row(i), E, T, S,
+                                    nETS, nNonSeasonal, nSeasonal, nArima, nXreg, nComponents, constant);
+
+        profilesRecent(indexLookupTable.col(i)) = adamFvalue(profilesRecent(indexLookupTable.col(i)),
+                                                             matrixF, E, T, S, nETS, nNonSeasonal, nSeasonal, nArima, nComponents, constant);
+    }
+
+    // return List::create(Named("matVt") = matrixVtnew, Named("yForecast") = vecYfor);
+    return vecYfor;
+}
+
 PYBIND11_MODULE(_adam_general, m)
 {
     m.doc() = "Adam code"; // module docstring
@@ -184,4 +212,19 @@ PYBIND11_MODULE(_adam_general, m)
         py::arg("vectorYt"),
         py::arg("vectorOt"),
         py::arg("backcast"));
+    m.def("adam_forecaster", &adamForecaster, "forecasts the adam model",
+          py::arg("matrixWt"),
+          py::arg("matrixF"),
+          py::arg("lags"),
+          py::arg("indexLookupTable"),
+          py::arg("profilesRecent"),
+          py::arg("E"),
+          py::arg("T"),
+          py::arg("S"),
+          py::arg("nNonSeasonal"),
+          py::arg("nSeasonal"),
+          py::arg("nArima"),
+          py::arg("nXreg"),
+          py::arg("constant"),
+          py::arg("horizon"));
 }