{ "arguments": { "data_x": { "type_value": "Array", "description": "Predictor variable" }, "data_y": { "type_value": "Array", "description": "Target variable" }, "k": { "type_value": "Integer", "default_python": "None", "default_rust": "None", "description": "Number of matchings. Memory usage is quadratic in K." }, "lower_slope": { "type_value": "Array", "default_python": "None", "default_rust": "None", "description": "Estimated minimum possible value of the slope." }, "upper_slope": { "type_value": "Array", "default_python": "None", "default_rust": "None", "description": "Estimated maximum possible value of the slope." }, "lower_intercept": { "type_value": "Array", "default_python": "None", "default_rust": "None", "description": "Estimated minimum possible value of the intercept." }, "upper_intercept": { "type_value": "Array", "default_python": "None", "default_rust": "None", "description": "Estimated maximum possible value of the intercept." } }, "id": "DPLinearRegression", "name": "dp_linear_regression", "options": { "implementation": { "type_proto": "string", "type_rust": "String", "default_python": "\"theil-sen-k-match\"", "default_rust": "String::from(\"theil-sen-k-match\")", "description": "Theil-Sen implementation to use. One of [`theil-sen`, `theil-sen-k-match`]" }, "privacy_usage": { "type_proto": "repeated PrivacyUsage", "type_rust": "Vec", "default_python": "None", "description": "Object describing the type and amount of privacy to be used for the mechanism release." } }, "return": { "type_value": "Array", "description": "Differentially private estimate of the slope and intercept of the line fit to the data." }, "description": "Returns differentially private estimates of the slope and intercept.", "proto_id": 67 }