* @brief Evaluate the model for a new set of new points
...
...
@@ -145,7 +145,7 @@ public:
* @param x_in a vector of pointers to the set of values for new data points (one pointer per each feature and order the same as appending the results of _feats[nn]->get_x_in_expr_list() for all feature)
* @return The prediction of the model for a given set of data points
* @brief Evaluate the model for a set of new points
...
...
@@ -153,7 +153,7 @@ public:
* @param x_in The set data for a set of new data points (size of n_feature x n_points, and order the same as appending the results of _feats[nn]->get_x_in_expr_list() for all feature)
* @return The prediction of the model for a given data point
* @brief Evaluate the model for a set of new points
...
...
@@ -161,7 +161,7 @@ public:
* @param x_in_dct The set of data points to evaluate the model. Keys must be strings representing feature expressions and vectors must be the same length
* @return The prediction of the model for a given data point
* @brief Evaluate the model for a set of new points
...
...
@@ -315,7 +315,7 @@ public:
* @param x_in The set data for a set of new data points (size of n_feature x n_points, and order the same as appending the results of _feats[nn]->get_x_in_expr_list() for all feature)
* @return The prediction of the model for a given data point
*/
np::ndarrayeval_many_py(np::ndarrayx_in);
np::ndarrayeval_many_py(np::ndarrayx_in)const;
/**
* @brief Evaluate the model for a set of new points
...
...
@@ -323,7 +323,7 @@ public:
* @param x_in_dct The set of data points to evaluate the model. Keys must be strings representing feature expressions and vectors must be the same length
* @return The prediction of the model for a given data point
throwstd::logic_error("The file does not have the same convex overlap ("+std::to_string(file_train_n_convex_overlap)+") as calculated here ("+std::to_string(_train_n_convex_overlap)+").");
* @param x_in pointer to the new data point (order the same as appending the results of _feats[nn]->get_x_in_expr_list() for all feature)
* @return The prediction of the model for a given data point
*/
doubleeval(double*x_in);
doubleeval(double*x_in)const;
/**
* @brief Evaluate the model for a new set of new points
...
...
@@ -137,7 +137,7 @@ public:
* @param x_in a vector of pointers to the set of values for new data points (one pointer per each feature and order the same as appending the results of _feats[nn]->get_x_in_expr_list() for all feature)
* @return The prediction of the model for a given set of data points
* @param fix_intercept if True then the intercept is 0.0
*/
ModelLogRegressor(
std::stringprop_label,
Unitprop_unit,
std::vector<double>prop_train,
std::vector<double>prop_test,
std::vector<model_node_ptr>feats,
std::vector<int>task_sizes_train,
std::vector<int>task_sizes_test,
boolfix_intercept
conststd::stringprop_label,
constUnitprop_unit,
conststd::vector<double>prop_train,
conststd::vector<double>prop_test,
conststd::vector<model_node_ptr>feats,
conststd::vector<int>task_sizes_train,
conststd::vector<int>task_sizes_test,
constboolfix_intercept
);
// DocString: model_log_reg_init_str
...
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@@ -119,7 +119,7 @@ public:
* @param x_in pointer to the new data point (order the same as appending the results of _feats[nn]->get_x_in_expr_list() for all feature)
* @return The prediction of the model for a given data point
*/
doubleeval(double*x_in);
doubleeval(double*x_in)const;
/**
* @brief Evaluate the model for a new set of new points
...
...
@@ -127,7 +127,7 @@ public:
* @param x_in a vector of pointers to the set of values for new data points (one pointer per each feature and order the same as appending the results of _feats[nn]->get_x_in_expr_list() for all feature)
* @return The prediction of the model for a given set of data points