import numpy as np
from scipy.stats import norm
from scipy.stats import beta
from matplotlib import mlab

# d-prime
def dprime(confusion_matrix):
    """
    Function takes in a 2x2 confusion matrix and returns the d-prime value for the predictions.

    d' = z(hit rate)-z(false alarm rate)

    http://en.wikipedia.org/wiki/D'
    """
    if max(confusion_matrix.shape) > 2:
        return False
    else:
        hit_rate = confusion_matrix[0, 0] / confusion_matrix[0, :].sum()
        fa_rate = confusion_matrix[1, 0] / confusion_matrix[1, :].sum()

        nudge = 0.0001
        if (hit_rate >= 1): hit_rate = 1 - nudge
        if (hit_rate <= 0): hit_rate = 0 + nudge
        if (fa_rate >= 1): fa_rate = 1 - nudge
        if (fa_rate <= 0): fa_rate = 0 + nudge

        dp = norm.ppf(hit_rate)-norm.ppf(fa_rate)
        return dp


# accuracy (% correct)
def acc(confusion_matrix):
    """Function takes in a NxN confusion matrix
    and returns the fraction of correct predictions"""

    x = confusion_matrix.diagonal().sum()
    N = confusion_matrix.sum()
    p = x / N

    return p


# accuracy (% correct)
def acc_ci(confusion_matrix, alpha=0.05):
    """Function takes in a NxN confusion matrix
    and returns the fraction of correct predictions"""

    x = confusion_matrix.diagonal().sum()
    N = confusion_matrix.sum()

    ci = beta.interval(1-alpha,x,N-x)
    return ci


# matthew's correlation coefficient
def mcc(confusion_matrix):
    """Function takes in a 2x2 confusion matrix
    and returns the Matthew's Correlation Coefficient for the predictions.

    MCC = (TP*TN-FP*FN)/sqrt((TP+FP)*(TP+FN)*(TN+FP)*(TN+FN))

    http://en.wikipedia.org/wiki/Matthews_correlation_coefficient

    """
    if max(confusion_matrix.shape) > 2:
        return False
    else:
        true_pos = confusion_matrix[0, 0]
        true_neg = confusion_matrix[1, 1]
        false_pos = confusion_matrix[1, 0]
        false_neg = confusion_matrix[0, 1]
        a = (true_pos*true_neg-false_pos*false_neg)/np.sqrt((true_pos+false_pos)*(true_pos+false_neg)*(true_neg+false_pos)*(true_neg+false_neg))
        return a

def create_conf_matrix(expected, predicted):
    """
    Function takes in a 1-D array of expected values and a 1-D array of predictions
    and returns a confusion matrix with size corresponding to the number of classes.

    http://en.wikipedia.org/wiki/Confusion_matrix

    Keyword arguments:
    expected  -- list of expected or true values
    predicted -- list of predicted or response values

    Returns the confusion matrix as a numpy array m[expectation,prediction]

    """
    n_classes = max(len(set(expected)), len(set(predicted)), 2)

    m = np.zeros((n_classes, n_classes))
    for exp, pred in zip(expected, predicted):
        m[exp,pred] += 1
    return m

class Performance():
    """ use this to compute performance metrics """
    def __init__(self, expected, predicted):
        self.confusion_matrix = create_conf_matrix(expected, predicted)
    def n_classes(self):
        return max(self.confusion_matrix.shape)
    def dprime(self):
        return dprime(self.confusion_matrix)
    def acc(self):
        return acc(self.confusion_matrix)
    def mcc(self):
        return mcc(self.confusion_matrix)




@@ -3177,32 +3177,99 @@
 nfusion_matrix)%0A
+    def acc_ci(self):%0A        return acc_ci(self.confusion_matrix)%0A
     def mcc(self

add acc_ci() method to Performance class