Split sma & adam_profile

python/CMakeLists.txt

@@ -12,25 +12,45 @@ if(SKBUILD)
 
 # Pybind11
 find_package(pybind11 REQUIRED)
-# Armadillo
-find_package(armadillo REQUIRED)
+
+# CARMA
+ADD_SUBDIRECTORY(../src/libs/carma carma)
+
+# BLAS and LAPACK. Needed by Armadillo
+find_package(BLAS)
+find_package(LAPACK)
+if(LAPACK_FOUND AND BLAS_FOUND)
+   set(lapackblas_libraries ${BLAS_LIBRARIES} ${LAPACK_LIBRARIES})
+else()
+   # IS: This is where they are on my system. This might change from one OS to another
+   set(lapackblas_libraries "/usr/lib/x86_64-linux-gnu/")
+endif()
+
+# Armadillo
+find_package(armadillo)
+IF(NOT ARMADILLO_FOUND)
+    set(ARMADILLO_INCLUDE_DIRS "/usr/lib/")
+ENDIF()
 include_directories(${ARMADILLO_INCLUDE_DIRS})
 
+# Adam General
+pybind11_add_module(_adam_general ../src/python_examples/adamGeneral.cpp)
+target_include_directories(_adam_general PRIVATE ../src/python_examples/.)
+target_link_libraries(_adam_general PRIVATE carma::carma ${ARMADILLO_LIBRARIES} ${lapackblas_libraries})
+install(TARGETS _adam_general DESTINATION smooth/adam_general)
+
+
+# Old experimental stuff by Leo
 # code to add the pybind11 cpp module, look at demo project 
 # (https://github.com/ltsaprounis/python-cpp-experiments/tree/main) for details.
-pybind11_add_module(_my_linalg ../src/python_examples/my_linalg.cpp)
+#pybind11_add_module(_my_linalg ../src/python_examples/my_linalg.cpp)
 
 # add CARMA as a subdirectory
-add_subdirectory(../src/libs/carma build)
-target_link_libraries(_my_linalg 
-    PRIVATE carma::carma
-    ${ARMADILLO_LIBRARIES}
-)
+#add_subdirectory(../src/libs/carma build)
+#target_link_libraries(_my_linalg
+#    PRIVATE carma::carma
+#    ${ARMADILLO_LIBRARIES}
+#)
 
-install(TARGETS _my_linalg DESTINATION smooth/my_linalg)
+# install(TARGETS _my_linalg DESTINATION smooth/my_linalg)
 
-# Adam General
-pybind11_add_module(_adam_general ../src/python_examples/adamGeneral.cpp)
-target_link_libraries(_adam_general PRIVATE carma::carma ${ARMADILLO_LIBRARIES})
-target_include_directories(_adam_general PRIVATE ../src/python_examples/.)
-install(TARGETS _adam_general DESTINATION smooth/adam_general)

python/smooth/adam_general/adam_profile.py

@@ -0,0 +1,63 @@
+import numpy as np
+
+
+def adamProfileCreator(
+    lagsModelAll, lagsModelMax, obsAll, lags=None, yIndex=None, yClasses=None
+):
+    """
+    Creates recent profile and the lookup table for adam.
+    Parameters:
+    lagsModelAll (list): All lags used in the model for ETS + ARIMA + xreg.
+    lagsModelMax (int): The maximum lag used in the model.
+    obsAll (int): Number of observations to create.
+    lags (list): The original lags provided by user (optional).
+    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.
+    """
+    # 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))
+        .T
+    )
+
+    # Update matrices based on lagsModelAll
+    for i, lag in enumerate(lagsModelAll):
+        # 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)
+
+        # 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
+            np.tile(
+                profileIndices[i, : lagsModelAll[i][0]],
+                int(np.ceil(obsAll / lagsModelAll[i][0])),
+            )[0:obsAll]
+            - 1
+        )
+
+        # 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
+        indexLookupTable[i, :lagsModelMax] = np.tile(unique_values, lagsModelMax)[
+            -lagsModelMax:
+        ]
+
+    # Convert to int!
+    indexLookupTable = indexLookupTable.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"),
+    }

python/smooth/adam_general/sma.py

@@ -1,5 +1,6 @@
 import numpy as np
 from smooth.adam_general._adam_general import adam_fitter
+from smooth.adam_general.adam_profile import adamProfileCreator
 
 
 def sma(y, order=1, h=10, holdout=False):
@@ -24,21 +25,25 @@ 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).reshape(order, 1)
-        lags_model_max = max(lags_model_all)
+        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 = np.zeros(
+        profiles_recent_table, index_lookup_table = adamProfileCreator(
+            lagsModelAll=lags_model_all,
+            lagsModelMax=lags_model_max,
+            obsAll=obs_all
+        ).values()
+
+        # # This needs to be generated by a profileCreator() function
+        # profiles_recent_table = np.mean(y_in_sample[0 : (order - 1)]) * np.ones(
         #     shape=(order, lags_model_max), dtype=np.float64
         # )
 
-        # This needs to be generated by a profileCreator() function
-        profiles_recent_table = np.mean(y_in_sample[0 : (order - 1)]) * np.ones(
-            shape=(order, lags_model_max), dtype=np.float64
-        )
-
-        # This as well...
-        index_lookup_table = np.tile(np.arange(order), (obs_all + lags_model_max, 1)).T
+        # # This as well...
+        # index_lookup_table = np.tile(
+        #     np.arange(order), (obs_all + lags_model_max, 1)
+        # ).T
 
         matF = np.ones((order, order)) / order
         matWt = np.ones((obs_in_sample, order))
@@ -72,64 +77,3 @@ def creator_sma(order):
         return adam_fitted
 
     return creator_sma(order=order)
-
-
-def adamProfileCreator(
-    lagsModelAll, lagsModelMax, obsAll, lags=None, yIndex=None, yClasses=None
-):
-    """
-    Creates recent profile and the lookup table for adam.
-
-    Parameters:
-    lagsModelAll (list): All lags used in the model for ETS + ARIMA + xreg.
-    lagsModelMax (int): The maximum lag used in the model.
-    obsAll (int): Number of observations to create.
-    lags (list): The original lags provided by user (optional).
-    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.
-    """
-    # 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))
-        .T
-    )
-
-    # Update matrices based on lagsModelAll
-    for i, lag in enumerate(lagsModelAll):
-        # 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] = np.arange(1, lag + 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)] = (  # noqa
-            np.tile(
-                profileIndices[i, : lagsModelAll[i]],
-                int(np.ceil(obsAll / lagsModelAll[i])),
-            )[0:obsAll]
-            - 1
-        )
-
-        # 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
-        indexLookupTable[i, :lagsModelMax] = np.tile(unique_values, lagsModelMax)[
-            -lagsModelMax:
-        ]
-
-    # Convert to int!
-    indexLookupTable = indexLookupTable.astype(int)
-
-    # Note: I skip andling of special cases (e.g., daylight saving time, leap years)
-    return {"recent": profilesRecentTable, "lookup": indexLookupTable}

python/smooth/adam_general/test_script.py

@@ -0,0 +1,7 @@
+import numpy as np
+from smooth.adam_general.sma import sma
+
+if __name__ == "__main__":
+    y = np.arange(0, 100)
+    results = sma(y, order=5)
+    print(results["yFitted"])