mirror of
https://github.com/Doctorado-ML/bayesclass.git
synced 2025-08-15 23:55:57 +00:00
Add predict_proba
This commit is contained in:
@@ -1318,6 +1318,8 @@ static std::vector<std::vector<int> > __pyx_convert_vector_from_py_std_3a__3a_v
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static std::string __pyx_convert_string_from_py_std__in_string(PyObject *); /*proto*/
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static std::vector<std::string> __pyx_convert_vector_from_py_std_3a__3a_string(PyObject *); /*proto*/
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static PyObject *__pyx_convert_vector_to_py_int(const std::vector<int> &); /*proto*/
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static PyObject *__pyx_convert_vector_to_py_double(const std::vector<double> &); /*proto*/
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static PyObject *__pyx_convert_vector_to_py_std_3a__3a_vector_3c_double_3e___(const std::vector<std::vector<double> > &); /*proto*/
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static CYTHON_INLINE PyObject *__pyx_convert_PyObject_string_to_py_std__in_string(std::string const &); /*proto*/
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static CYTHON_INLINE PyObject *__pyx_convert_PyUnicode_string_to_py_std__in_string(std::string const &); /*proto*/
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static CYTHON_INLINE PyObject *__pyx_convert_PyStr_string_to_py_std__in_string(std::string const &); /*proto*/
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@@ -1366,13 +1368,14 @@ static int __pyx_pf_10bayesclass_15cppBayesNetwork_12BayesNetwork___cinit__(stru
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static void __pyx_pf_10bayesclass_15cppBayesNetwork_12BayesNetwork_2__dealloc__(struct __pyx_obj_10bayesclass_15cppBayesNetwork_BayesNetwork *__pyx_v_self); /* proto */
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static PyObject *__pyx_pf_10bayesclass_15cppBayesNetwork_12BayesNetwork_4fit(struct __pyx_obj_10bayesclass_15cppBayesNetwork_BayesNetwork *__pyx_v_self, PyObject *__pyx_v_X, PyObject *__pyx_v_y, PyObject *__pyx_v_features, PyObject *__pyx_v_className); /* proto */
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static PyObject *__pyx_pf_10bayesclass_15cppBayesNetwork_12BayesNetwork_6predict(struct __pyx_obj_10bayesclass_15cppBayesNetwork_BayesNetwork *__pyx_v_self, PyObject *__pyx_v_X); /* proto */
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static PyObject *__pyx_pf_10bayesclass_15cppBayesNetwork_12BayesNetwork_8score(struct __pyx_obj_10bayesclass_15cppBayesNetwork_BayesNetwork *__pyx_v_self, PyObject *__pyx_v_X, PyObject *__pyx_v_y); /* proto */
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static PyObject *__pyx_pf_10bayesclass_15cppBayesNetwork_12BayesNetwork_10addNode(struct __pyx_obj_10bayesclass_15cppBayesNetwork_BayesNetwork *__pyx_v_self, PyObject *__pyx_v_name, PyObject *__pyx_v_states); /* proto */
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static PyObject *__pyx_pf_10bayesclass_15cppBayesNetwork_12BayesNetwork_12addEdge(struct __pyx_obj_10bayesclass_15cppBayesNetwork_BayesNetwork *__pyx_v_self, PyObject *__pyx_v_source, PyObject *__pyx_v_destination); /* proto */
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static PyObject *__pyx_pf_10bayesclass_15cppBayesNetwork_12BayesNetwork_14getFeatures(struct __pyx_obj_10bayesclass_15cppBayesNetwork_BayesNetwork *__pyx_v_self); /* proto */
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static PyObject *__pyx_pf_10bayesclass_15cppBayesNetwork_12BayesNetwork_16getClassName(struct __pyx_obj_10bayesclass_15cppBayesNetwork_BayesNetwork *__pyx_v_self); /* proto */
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static PyObject *__pyx_pf_10bayesclass_15cppBayesNetwork_12BayesNetwork_18getClassNumStates(struct __pyx_obj_10bayesclass_15cppBayesNetwork_BayesNetwork *__pyx_v_self); /* proto */
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static PyObject *__pyx_pf_10bayesclass_15cppBayesNetwork_12BayesNetwork_20__reduce__(CYTHON_UNUSED struct __pyx_obj_10bayesclass_15cppBayesNetwork_BayesNetwork *__pyx_v_self); /* proto */
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static PyObject *__pyx_pf_10bayesclass_15cppBayesNetwork_12BayesNetwork_8predict_proba(struct __pyx_obj_10bayesclass_15cppBayesNetwork_BayesNetwork *__pyx_v_self, PyObject *__pyx_v_X); /* proto */
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static PyObject *__pyx_pf_10bayesclass_15cppBayesNetwork_12BayesNetwork_10score(struct __pyx_obj_10bayesclass_15cppBayesNetwork_BayesNetwork *__pyx_v_self, PyObject *__pyx_v_X, PyObject *__pyx_v_y); /* proto */
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static PyObject *__pyx_pf_10bayesclass_15cppBayesNetwork_12BayesNetwork_12addNode(struct __pyx_obj_10bayesclass_15cppBayesNetwork_BayesNetwork *__pyx_v_self, PyObject *__pyx_v_name, PyObject *__pyx_v_states); /* proto */
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static PyObject *__pyx_pf_10bayesclass_15cppBayesNetwork_12BayesNetwork_14addEdge(struct __pyx_obj_10bayesclass_15cppBayesNetwork_BayesNetwork *__pyx_v_self, PyObject *__pyx_v_source, PyObject *__pyx_v_destination); /* proto */
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static PyObject *__pyx_pf_10bayesclass_15cppBayesNetwork_12BayesNetwork_16getFeatures(struct __pyx_obj_10bayesclass_15cppBayesNetwork_BayesNetwork *__pyx_v_self); /* proto */
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static PyObject *__pyx_pf_10bayesclass_15cppBayesNetwork_12BayesNetwork_18getClassName(struct __pyx_obj_10bayesclass_15cppBayesNetwork_BayesNetwork *__pyx_v_self); /* proto */
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static PyObject *__pyx_pf_10bayesclass_15cppBayesNetwork_12BayesNetwork_20getClassNumStates(struct __pyx_obj_10bayesclass_15cppBayesNetwork_BayesNetwork *__pyx_v_self); /* proto */
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static PyObject *__pyx_pf_10bayesclass_15cppBayesNetwork_12BayesNetwork_22__reduce__(CYTHON_UNUSED struct __pyx_obj_10bayesclass_15cppBayesNetwork_BayesNetwork *__pyx_v_self); /* proto */
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static PyObject *__pyx_tp_new_10bayesclass_15cppBayesNetwork_BayesNetwork(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
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static PyObject *__pyx_float_0_8;
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static PyObject *__pyx_float_1_0;
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@@ -1703,7 +1706,7 @@ static PyObject *__pyx_pf_10bayesclass_15cppBayesNetwork_12BayesNetwork_4fit(str
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* return self
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* def predict(self, X): # <<<<<<<<<<<<<<
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* return self.thisptr.predict(X)
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* # def predict_proba(self, X):
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* def predict_proba(self, X):
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*/
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/* Python wrapper */
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@@ -1733,8 +1736,8 @@ static PyObject *__pyx_pf_10bayesclass_15cppBayesNetwork_12BayesNetwork_6predict
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* return self
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* def predict(self, X):
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* return self.thisptr.predict(X) # <<<<<<<<<<<<<<
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* # def predict_proba(self, X):
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* # return self.thisptr.predict_proba(X)
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* def predict_proba(self, X):
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* return self.thisptr.predict_proba(X)
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*/
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__Pyx_XDECREF(__pyx_r);
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@@ -1749,7 +1752,7 @@ static PyObject *__pyx_pf_10bayesclass_15cppBayesNetwork_12BayesNetwork_6predict
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* return self
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* def predict(self, X): # <<<<<<<<<<<<<<
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* return self.thisptr.predict(X)
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* # def predict_proba(self, X):
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* def predict_proba(self, X):
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*/
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/* function exit code */
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@@ -1763,17 +1766,82 @@ static PyObject *__pyx_pf_10bayesclass_15cppBayesNetwork_12BayesNetwork_6predict
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return __pyx_r;
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}
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/* "bayesclass/BayesNetwork.pyx":37
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* def predict(self, X):
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* return self.thisptr.predict(X)
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* def predict_proba(self, X): # <<<<<<<<<<<<<<
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* return self.thisptr.predict_proba(X)
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* def score(self, X, y):
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*/
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/* Python wrapper */
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static PyObject *__pyx_pw_10bayesclass_15cppBayesNetwork_12BayesNetwork_9predict_proba(PyObject *__pyx_v_self, PyObject *__pyx_v_X); /*proto*/
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static PyObject *__pyx_pw_10bayesclass_15cppBayesNetwork_12BayesNetwork_9predict_proba(PyObject *__pyx_v_self, PyObject *__pyx_v_X) {
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static PyObject *__pyx_pf_10bayesclass_15cppBayesNetwork_12BayesNetwork_8predict_proba(struct __pyx_obj_10bayesclass_15cppBayesNetwork_BayesNetwork *__pyx_v_self, PyObject *__pyx_v_X) {
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/* "bayesclass/BayesNetwork.pyx":38
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* return self.thisptr.predict(X)
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* def predict_proba(self, X):
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* return self.thisptr.predict_proba(X) # <<<<<<<<<<<<<<
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* def score(self, X, y):
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* return self.thisptr.score(X, y)
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/* "bayesclass/BayesNetwork.pyx":37
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* def predict(self, X):
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* return self.thisptr.predict(X)
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* def predict_proba(self, X): # <<<<<<<<<<<<<<
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* return self.thisptr.predict_proba(X)
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* def score(self, X, y):
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*/
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/* function exit code */
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/* "bayesclass/BayesNetwork.pyx":39
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* # def predict_proba(self, X):
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* # return self.thisptr.predict_proba(X)
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* def predict_proba(self, X):
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* return self.thisptr.predict_proba(X)
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* def score(self, X, y): # <<<<<<<<<<<<<<
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* return self.thisptr.score(X, y)
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/* Python wrapper */
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static PyObject *__pyx_pw_10bayesclass_15cppBayesNetwork_12BayesNetwork_9score(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
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static PyObject *__pyx_pw_10bayesclass_15cppBayesNetwork_12BayesNetwork_11score(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
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static PyObject *__pyx_pw_10bayesclass_15cppBayesNetwork_12BayesNetwork_11score(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
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static PyObject *__pyx_pf_10bayesclass_15cppBayesNetwork_12BayesNetwork_8score(struct __pyx_obj_10bayesclass_15cppBayesNetwork_BayesNetwork *__pyx_v_self, PyObject *__pyx_v_X, PyObject *__pyx_v_y) {
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static PyObject *__pyx_pf_10bayesclass_15cppBayesNetwork_12BayesNetwork_10score(struct __pyx_obj_10bayesclass_15cppBayesNetwork_BayesNetwork *__pyx_v_self, PyObject *__pyx_v_X, PyObject *__pyx_v_y) {
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/* "bayesclass/BayesNetwork.pyx":40
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* return self.thisptr.predict_proba(X)
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* def score(self, X, y):
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* return self.thisptr.score(X, y) # <<<<<<<<<<<<<<
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* def addNode(self, name, states):
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@@ -1863,8 +1931,8 @@ static PyObject *__pyx_pf_10bayesclass_15cppBayesNetwork_12BayesNetwork_8score(s
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goto __pyx_L0;
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/* "bayesclass/BayesNetwork.pyx":39
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* # def predict_proba(self, X):
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* # return self.thisptr.predict_proba(X)
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* def predict_proba(self, X):
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* return self.thisptr.predict_proba(X)
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* def score(self, X, y): # <<<<<<<<<<<<<<
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* return self.thisptr.score(X, y)
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@@ -1890,8 +1958,8 @@ static PyObject *__pyx_pf_10bayesclass_15cppBayesNetwork_12BayesNetwork_8score(s
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Network(float, float) except +
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void fit(vector[vector[int]], vector[int], vector[string], string)
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vector[int] predict(vector[vector[int]])
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vector[vector[double]] predict_proba(vector[vector[int]])
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float score(const vector[vector[int]], const vector[int])
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|
||||
@@ -179,7 +183,7 @@ namespace bayesnet {
|
||||
}
|
||||
return (double)correct / y_pred.size();
|
||||
}
|
||||
pair<int, double> Network::predict_sample(const vector<int>& sample)
|
||||
vector<double> Network::predict_sample(const vector<int>& sample)
|
||||
{
|
||||
// Ensure the sample size is equal to the number of features
|
||||
if (sample.size() != features.size()) {
|
||||
@@ -190,14 +194,8 @@ namespace bayesnet {
|
||||
for (int i = 0; i < sample.size(); ++i) {
|
||||
evidence[features[i]] = sample[i];
|
||||
}
|
||||
vector<double> classProbabilities = exactInference(evidence);
|
||||
return exactInference(evidence);
|
||||
|
||||
// Find the class with the maximum posterior probability
|
||||
auto maxElem = max_element(classProbabilities.begin(), classProbabilities.end());
|
||||
int predictedClass = distance(classProbabilities.begin(), maxElem);
|
||||
double maxProbability = *maxElem;
|
||||
|
||||
return make_pair(predictedClass, maxProbability);
|
||||
}
|
||||
double Network::computeFactor(map<string, int>& completeEvidence)
|
||||
{
|
||||
|
||||
@@ -16,7 +16,7 @@ namespace bayesnet {
|
||||
string className;
|
||||
int laplaceSmoothing;
|
||||
bool isCyclic(const std::string&, std::unordered_set<std::string>&, std::unordered_set<std::string>&);
|
||||
pair<int, double> predict_sample(const vector<int>&);
|
||||
vector<double> predict_sample(const vector<int>&);
|
||||
vector<double> exactInference(map<string, int>&);
|
||||
double computeFactor(map<string, int>&);
|
||||
public:
|
||||
@@ -34,8 +34,9 @@ namespace bayesnet {
|
||||
string getClassName();
|
||||
void fit(const vector<vector<int>>&, const vector<int>&, const vector<string>&, const string&);
|
||||
vector<int> predict(const vector<vector<int>>&);
|
||||
vector<vector<float>> predict_proba(const vector<vector<int>>&);
|
||||
vector<vector<double>> predict_proba(const vector<vector<int>>&);
|
||||
double score(const vector<vector<int>>&, const vector<int>&);
|
||||
inline string version() { return "0.1.0"; }
|
||||
};
|
||||
}
|
||||
#endif
|
||||
Reference in New Issue
Block a user