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+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
+ )