GAIuS Data Format (GDF)
The data entry format for use with the GAIuS Framework is called GAIuS Data Format (GDF). GDFs have several fields that correspond to different types of data:
gdf = \
{'strings' : [],
'vectors': [],
'emotives': [],
'metadata': {},
'path': []
}
Fields
The following section briefly explain the different fields present in a GDF
strings
The strings field is used to observe data that is already symbolic (in string format) on a GAIuS Agent.
- Example:
gdf['strings'] = ['temperature|54', 'UV_INDEX|HIGH', 'weather|sunny', 'time|afternoon']
vectors
The vectors field is used to observe numeric data on a GAIuS Agent. For example, this field may be used for flattened image data, mathmatical data, etc.
- Example:
# image loaded previously into Python flattened_image_data = image.flatten() gdf['vectors'] = [flattened_image_data]
emotives
The emotives field is used to provide emotional data about an observation to the GAIuS agent.
metadata
The metadata field is used to pass through additional data about an observation that will not impact the predicting of a Cognitive Processor
path
The path field is used internally to show the path taken by an observation as it travels through a GAIuS agent’s topology
Example Conversions
To illustrate the process of converting data into GDF format, here are some specific examples corresponding to potential use-cases.
CSV to GDF
CSV Data can be rapidly converted into GDF format by utilizing the strings field of the GDF. Each row corresponds to a single data record, from which an individual GDF will be created. The headers, and the individual data elements from each row can be concatenated to make feature strings for each entry. For example, if a header value is “name”, and a specific data value is “Steve”, a feature string for the GDF would be “name|Steve”.
Here is a more comprehensive example:
Name |
Age |
Sex |
Salary |
---|---|---|---|
Steve |
34 |
male |
50000 |
Josh |
25 |
male |
65000 |
Gregory |
52 |
male |
43000 |
Strings |
Vectors |
Emotives |
---|---|---|
|
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|
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