from gtnlplib.constants import OFFSET
from gtnlplib import clf_base, evaluation

import numpy as np
from collections import defaultdict

# deliverable 3.1
def get_corpus_counts(x,y,label):
    """Compute corpus counts of words for all documents with a given label.

    :param x: list of counts, one per instance
    :param y: list of labels, one per instance
    :param label: desired label for corpus counts
    :returns: defaultdict of corpus counts
    :rtype: defaultdict

    """

    raise NotImplementedError

# deliverable 3.2
def estimate_pxy(x,y,label,smoothing,vocab):
    '''
    Compute smoothed log-probability P(word | label) for a given label.

    :param x: list of counts, one per instance
    :param y: list of labels, one per instance
    :param label: desired label
    :param smoothing: additive smoothing amount
    :param vocab: list of words in vocabulary
    :returns: defaultdict of log probabilities per word
    :rtype: defaultdict of log probabilities per word

    '''

    raise NotImplementedError

# deliverable 3.3
def estimate_nb(x,y,smoothing):
    """estimate a naive bayes model

    :param x: list of dictionaries of base feature counts
    :param y: list of labels
    :param smoothing: smoothing constant
    :returns: weights
    :rtype: defaultdict 

    """
    
    labels = set(y)
    counts = defaultdict(float)
    doc_counts = defaultdict(float)
    
    raise NotImplementedError

# deliverable 3.4
def find_best_smoother(x_tr,y_tr,x_dv,y_dv,smoothers):
    '''
    find the smoothing value that gives the best accuracy on the dev data

    :param x_tr: training instances
    :param y_tr: training labels
    :param x_dv: dev instances
    :param y_dv: dev labels
    :param smoothers: list of smoothing values
    :returns: best smoothing value
    :rtype: float

    '''

    raise NotImplementedError