#!/usr/bin/python3
import statistics as stats


class Encoder:

    def __init__(self, unique_values, is_categorical):
        """
        Constructor of an Encoder using one-hot-encoding
        """

        self.is_categorical = is_categorical
        self.is_binary = len(unique_values) == 2
        self.unique_values = unique_values
        self.min = min(unique_values)
        self.max = max(unique_values)


    def __get_stdev_band(self, unique_values):
        """
        Get the lower bound and upper bound for the standard devaitation band
        for continuous value. 
        """

        mean = stats.mean(unique_values)
        stdev = stats.stdev(unique_values)
        return [mean - stdev, mean + stdev]

    def __normalize(self, value, lower_bound, upper_bound):
        """
        Normalize the value to the lower bound and upper bound by the max & min
        """

        min_max_diff = self.max - self.min
        bound_diff = upper_bound - lower_bound
        return (value - self.min) / min_max_diff * bound_diff + lower_bound


    def encode(self, value):
        """
        Get one-hot encoding for a value based on the 
        unique values in this encoder.

        Return a list of 0s except 1 at the index that matches the unique value
        index.
        """

        encoded = []
        if self.is_categorical:
            if self.is_binary:
                encoded.append(0 if value == self.unique_values[0] else 1)
            else: 
                for index in range(len(self.unique_values)):
                    unique = self.unique_values[index]
                    encoded.append(1 if value == unique else 0)
        else: # continuous data
            normalized = self.__normalize(value, -1, 1)
            encoded.append(normalized)
        

        return encoded
55ae62165f757289721c3796b3aaa2206ff3ad2a
<mask token>


class Encoder:

    def __init__(self, unique_values, is_categorical):
        """
        Constructor of an Encoder using one-hot-encoding
        """
        self.is_categorical = is_categorical
        self.is_binary = len(unique_values) == 2
        self.unique_values = unique_values
        self.min = min(unique_values)
        self.max = max(unique_values)

    def __get_stdev_band(self, unique_values):
        """
        Get the lower bound and upper bound for the standard devaitation band
        for continuous value. 
        """
        mean = stats.mean(unique_values)
        stdev = stats.stdev(unique_values)
        return [mean - stdev, mean + stdev]
    <mask token>
    <mask token>

<mask token>


class Encoder:

    def __init__(self, unique_values, is_categorical):
        """
        Constructor of an Encoder using one-hot-encoding
        """
        self.is_categorical = is_categorical
        self.is_binary = len(unique_values) == 2
        self.unique_values = unique_values
        self.min = min(unique_values)
        self.max = max(unique_values)

    def __get_stdev_band(self, unique_values):
        """
        Get the lower bound and upper bound for the standard devaitation band
        for continuous value. 
        """
        mean = stats.mean(unique_values)
        stdev = stats.stdev(unique_values)
        return [mean - stdev, mean + stdev]

    def __normalize(self, value, lower_bound, upper_bound):
        """
        Normalize the value to the lower bound and upper bound by the max & min
        """
        min_max_diff = self.max - self.min
        bound_diff = upper_bound - lower_bound
        return (value - self.min) / min_max_diff * bound_diff + lower_bound
    <mask token>

<mask token>


class Encoder:

    def __init__(self, unique_values, is_categorical):
        """
        Constructor of an Encoder using one-hot-encoding
        """
        self.is_categorical = is_categorical
        self.is_binary = len(unique_values) == 2
        self.unique_values = unique_values
        self.min = min(unique_values)
        self.max = max(unique_values)

    def __get_stdev_band(self, unique_values):
        """
        Get the lower bound and upper bound for the standard devaitation band
        for continuous value. 
        """
        mean = stats.mean(unique_values)
        stdev = stats.stdev(unique_values)
        return [mean - stdev, mean + stdev]

    def __normalize(self, value, lower_bound, upper_bound):
        """
        Normalize the value to the lower bound and upper bound by the max & min
        """
        min_max_diff = self.max - self.min
        bound_diff = upper_bound - lower_bound
        return (value - self.min) / min_max_diff * bound_diff + lower_bound

    def encode(self, value):
        """
        Get one-hot encoding for a value based on the 
        unique values in this encoder.

        Return a list of 0s except 1 at the index that matches the unique value
        index.
        """
        encoded = []
        if self.is_categorical:
            if self.is_binary:
                encoded.append(0 if value == self.unique_values[0] else 1)
            else:
                for index in range(len(self.unique_values)):
                    unique = self.unique_values[index]
                    encoded.append(1 if value == unique else 0)
        else:
            normalized = self.__normalize(value, -1, 1)
            encoded.append(normalized)
        return encoded

import statistics as stats


class Encoder:

    def __init__(self, unique_values, is_categorical):
        """
        Constructor of an Encoder using one-hot-encoding
        """
        self.is_categorical = is_categorical
        self.is_binary = len(unique_values) == 2
        self.unique_values = unique_values
        self.min = min(unique_values)
        self.max = max(unique_values)

    def __get_stdev_band(self, unique_values):
        """
        Get the lower bound and upper bound for the standard devaitation band
        for continuous value. 
        """
        mean = stats.mean(unique_values)
        stdev = stats.stdev(unique_values)
        return [mean - stdev, mean + stdev]

    def __normalize(self, value, lower_bound, upper_bound):
        """
        Normalize the value to the lower bound and upper bound by the max & min
        """
        min_max_diff = self.max - self.min
        bound_diff = upper_bound - lower_bound
        return (value - self.min) / min_max_diff * bound_diff + lower_bound

    def encode(self, value):
        """
        Get one-hot encoding for a value based on the 
        unique values in this encoder.

        Return a list of 0s except 1 at the index that matches the unique value
        index.
        """
        encoded = []
        if self.is_categorical:
            if self.is_binary:
                encoded.append(0 if value == self.unique_values[0] else 1)
            else:
                for index in range(len(self.unique_values)):
                    unique = self.unique_values[index]
                    encoded.append(1 if value == unique else 0)
        else:
            normalized = self.__normalize(value, -1, 1)
            encoded.append(normalized)
        return encoded

#!/usr/bin/python3
import statistics as stats


class Encoder:

    def __init__(self, unique_values, is_categorical):
        """
        Constructor of an Encoder using one-hot-encoding
        """

        self.is_categorical = is_categorical
        self.is_binary = len(unique_values) == 2
        self.unique_values = unique_values
        self.min = min(unique_values)
        self.max = max(unique_values)


    def __get_stdev_band(self, unique_values):
        """
        Get the lower bound and upper bound for the standard devaitation band
        for continuous value. 
        """

        mean = stats.mean(unique_values)
        stdev = stats.stdev(unique_values)
        return [mean - stdev, mean + stdev]

    def __normalize(self, value, lower_bound, upper_bound):
        """
        Normalize the value to the lower bound and upper bound by the max & min
        """

        min_max_diff = self.max - self.min
        bound_diff = upper_bound - lower_bound
        return (value - self.min) / min_max_diff * bound_diff + lower_bound


    def encode(self, value):
        """
        Get one-hot encoding for a value based on the 
        unique values in this encoder.

        Return a list of 0s except 1 at the index that matches the unique value
        index.
        """

        encoded = []
        if self.is_categorical:
            if self.is_binary:
                encoded.append(0 if value == self.unique_values[0] else 1)
            else: 
                for index in range(len(self.unique_values)):
                    unique = self.unique_values[index]
                    encoded.append(1 if value == unique else 0)
        else: # continuous data
            normalized = self.__normalize(value, -1, 1)
            encoded.append(normalized)
        

        return encoded