made progress in binaryclassifier rewrite, restructured file tree

1 files changed, 45 insertions, 0 deletions

diff --git a/ricky/dataset.py b/ricky/dataset.py
new file mode 100644
index 0000000..4f8a422
--- /dev/null
+++ b/ricky/dataset.py
@@ -0,0 +1,45 @@
+import ricky.params
+from ricky.utils import data_from_image
+from pybrain.datasets import SupervisedDataSet
+
+
+# while subclassing this works, we should try to detect the length of params
+# and build a new data set for each type of params set...
+# therefore, an instance of SupervisedDataSet could actually be
+# accessed through the params instance...simplified one-to-one mapping
+
+# we are limited to only one classifier per params instance as well
+# however this is sort of a good thing, because built into the params
+# class can be a method that randomizes params, and then evaluates
+
+# we might be able to get this done through multiple inheritance
+# keep all dataset related stuff in a separate class to make it better organized
+
+# we need
+# .evaluate
+# .generate_liked_image
+# .train_from_url_list
+# .reset
+
+
+class DataSet(SupervisedDataSet):
+
+    @staticmethod
+    def _file_into_list(self, filepath):
+        f = open(filepath, "r")
+        return f.read().split("\n")
+
+    def _load_url_list(self, url_list, liked=False):
+        target = 0
+        if liked:
+            target = 1
+        data_list = [data_from_image(image) for image in url_list if image]
+        for data in data_list:
+            for params_class in ricky.params.Params.__subclasses__():
+                if data['module'] == params_class.__name__:
+                    params_instance = params_class()
+                    params_instance.from_dict(data['params'])
+                    self.addSample(
+                        params_instance.as_normalized(),
+                        target
+                    )