from sklearn.utils._typedefs cimport float32_t, float64_t, intp_t, uint8_t, uint32_t


ctypedef float64_t X_DTYPE_C
ctypedef uint8_t X_BINNED_DTYPE_C
ctypedef float64_t Y_DTYPE_C
ctypedef float32_t G_H_DTYPE_C
ctypedef uint32_t BITSET_INNER_DTYPE_C
ctypedef BITSET_INNER_DTYPE_C[8] BITSET_DTYPE_C


cdef packed struct hist_struct:
    # Same as histogram dtype but we need a struct to declare views. It needs
    # to be packed since by default numpy dtypes aren't aligned
    Y_DTYPE_C sum_gradients
    Y_DTYPE_C sum_hessians
    unsigned int count


cdef packed struct node_struct:
    # Equivalent struct to PREDICTOR_RECORD_DTYPE to use in memory views. It
    # needs to be packed since by default numpy dtypes aren't aligned
    Y_DTYPE_C value
    unsigned int count
    intp_t feature_idx
    X_DTYPE_C num_threshold
    uint8_t missing_go_to_left
    unsigned int left
    unsigned int right
    Y_DTYPE_C gain
    unsigned int depth
    uint8_t is_leaf
    X_BINNED_DTYPE_C bin_threshold
    uint8_t is_categorical
    # The index of the corresponding bitsets in the Predictor's bitset arrays.
    # Only used if is_categorical is True
    unsigned int bitset_idx


cpdef enum MonotonicConstraint:
    NO_CST = 0
    POS = 1
    NEG = -1