// <copyright file="Statistics.cs" company="Math.NET">
// Math.NET Numerics, part of the Math.NET Project
// http://mathnet.opensourcedotnet.info
//
// Copyright (c) 2009 Math.NET
//
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// OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
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namespace MathNet.Numerics.Statistics
{
    using System;
    using System.Collections.Generic;
    using Properties;

    /// <summary>
    /// Extension methods to return basic statistics on set of data.
    /// </summary>
    public static class Statistics
    {
        /// <summary>
        /// Calculates the sample mean.
        /// </summary>
        /// <param name="data">The data to calculate the mean of.</param>
        /// <returns>The mean of the sample.</returns>
        public static double Mean(this IEnumerable<double> data)
        {
            if (data == null)
            {
                throw new ArgumentNullException("data");
            }

            double mean = 0;
            int m = 0;
            foreach (var item in data)
            {
                mean += (item - mean) / ++m;
            }

            return mean;
        }

        /// <summary>
        /// Calculates the sample mean.
        /// </summary>
        /// <param name="data">The data to calculate the mean of.</param>
        /// <returns>The mean of the sample.</returns>
        public static double Mean(this IEnumerable<double?> data)
        {
            if (data == null)
            {
                throw new ArgumentNullException("data");
            }

            double mean = 0;
            int m = 0;
            foreach (var item in data)
            {
                if (item.HasValue)
                {
                    mean += (item.Value - mean) / ++m;
                }
            }

            return mean;
        }

        /// <summary>
        /// Calculates the unbiased population variance estimator (on a dataset of size N will use an N-1 normalizer).
        /// </summary>
        /// <param name="data">The data to calculate the variance of.</param>
        /// <returns>The unbiased population variance of the sample.</returns>
        public static double Variance(this IEnumerable<double> data)
        {
            if (data == null)
            {
                throw new ArgumentNullException("data");
            }

            double variance = 0;
            double t = 0;
            int j = 0;

            IEnumerator<double> iterator = data.GetEnumerator();
            if (iterator.MoveNext())
            {
                j++;
                t = iterator.Current;
            }

            while (iterator.MoveNext())
            {
                j++;
                double xi = iterator.Current;
                t += xi;
                double diff = (j * xi) - t;
                variance += (diff * diff) / (j * (j - 1));
            }

            return variance / (j - 1);
        }

        /// <summary>
        /// Computes the unbiased population variance estimator (on a dataset of size N will use an N-1 normalizer) for nullable data.
        /// </summary>
        /// <param name="data">The data to calculate the variance of.</param>
        /// <returns>The population variance of the sample.</returns>
        public static double Variance(this IEnumerable<double?> data)
        {
            if (data == null)
            {
                throw new ArgumentNullException("data");
            }

            double variance = 0;
            double t = 0;
            int j = 0;

            IEnumerator<double?> iterator = data.GetEnumerator();

            while (true)
            {
                bool hasNext = iterator.MoveNext();
                if (!hasNext)
                {
                    break;
                }

                if (iterator.Current.HasValue)
                {
                    j++;
                    t = iterator.Current.Value;
                    break;
                }
            }

            while (iterator.MoveNext())
            {
                if (iterator.Current.HasValue)
                {
                    j++;
                    double xi = iterator.Current.Value;
                    t += xi;
                    double diff = (j * xi) - t;
                    variance += (diff * diff) / (j * (j - 1));
                }
            }

            return variance / (j - 1);
        }

        /// <summary>
        /// Calculates the biased population variance estimator (on a dataset of size N will use an N normalizer).
        /// </summary>
        /// <param name="data">The data to calculate the variance of.</param>
        /// <returns>The biased population variance of the sample.</returns>
        public static double PopulationVariance(this IEnumerable<double> data)
        {
            if (data == null)
            {
                throw new ArgumentNullException("data");
            }

            double variance = 0;
            double t = 0;
            int j = 0;

            IEnumerator<double> iterator = data.GetEnumerator();
            if (iterator.MoveNext())
            {
                j++;
                t = iterator.Current;
            }

            while (iterator.MoveNext())
            {
                j++;
                double xi = iterator.Current;
                t += xi;
                double diff = (j * xi) - t;
                variance += (diff * diff) / (j * (j - 1));
            }

            return variance / j;
        }

        /// <summary>
        /// Computes the biased population variance estimator (on a dataset of size N will use an N normalizer) for nullable data.
        /// </summary>
        /// <param name="data">The data to calculate the variance of.</param>
        /// <returns>The population variance of the sample.</returns>
        public static double PopulationVariance(this IEnumerable<double?> data)
        {
            if (data == null)
            {
                throw new ArgumentNullException("data");
            }

            double variance = 0;
            double t = 0;
            int j = 0;

            IEnumerator<double?> iterator = data.GetEnumerator();

            while (true)
            {
                bool hasNext = iterator.MoveNext();
                if (!hasNext)
                {
                    break;
                }

                if (iterator.Current.HasValue)
                {
                    j++;
                    t = iterator.Current.Value;
                    break;
                }
            }

            while (iterator.MoveNext())
            {
                if (iterator.Current.HasValue)
                {
                    j++;
                    double xi = iterator.Current.Value;
                    t += xi;
                    double diff = (j * xi) - t;
                    variance += (diff * diff) / (j * (j - 1));
                }
            }

            return variance / j;
        }

        /// <summary>
        /// Calculates the unbiased sample standard deviation (on a dataset of size N will use an N-1 normalizer).
        /// </summary>
        /// <param name="data">The data to calculate the standard deviation of.</param>
        /// <returns>The standard deviation of the sample.</returns>
        public static double StandardDeviation(this IEnumerable<double> data)
        {
            if (data == null)
            {
                throw new ArgumentNullException("data");
            }

            return Math.Sqrt(Variance(data));
        }

        /// <summary>
        /// Calculates the unbiased sample standard deviation (on a dataset of size N will use an N-1 normalizer).
        /// </summary>
        /// <param name="data">The data to calculate the standard deviation of.</param>
        /// <returns>The standard deviation of the sample.</returns>
        public static double StandardDeviation(this IEnumerable<double?> data)
        {
            if (data == null)
            {
                throw new ArgumentNullException("data");
            }

            return Math.Sqrt(Variance(data));
        }

        /// <summary>
        /// Calculates the biased sample standard deviation (on a dataset of size N will use an N normalizer).
        /// </summary>
        /// <param name="data">The data to calculate the standard deviation of.</param>
        /// <returns>The standard deviation of the sample.</returns>
        public static double PopulationStandardDeviation(this IEnumerable<double> data)
        {
            if (data == null)
            {
                throw new ArgumentNullException("data");
            }

            return Math.Sqrt(PopulationVariance(data));
        }

        /// <summary>
        /// Calculates the biased sample standard deviation (on a dataset of size N will use an N normalizer).
        /// </summary>
        /// <param name="data">The data to calculate the standard deviation of.</param>
        /// <returns>The standard deviation of the sample.</returns>
        public static double PopulationStandardDeviation(this IEnumerable<double?> data)
        {
            if (data == null)
            {
                throw new ArgumentNullException("data");
            }

            return Math.Sqrt(PopulationVariance(data));
        }

        /// <summary>
        /// Returns the minimum value in the sample data.
        /// </summary>
        /// <param name="data">The sample data.</param>
        /// <returns>The minimum value in the sample data.</returns>
        public static double Minimum(this IEnumerable<double?> data)
        {
            if (data == null)
            {
                throw new ArgumentNullException("data");
            }

            double min = double.MaxValue;
            int count = 0;
            foreach (double? d in data)
            {
                if (d.HasValue)
                {
                    min = Math.Min(min, d.Value);
                    count++;
                }
            }

            if (count == 0)
            {
                throw new ArgumentException(Resources.CollectionEmpty, "data");
            }

            return min;
        }

        /// <summary>
        /// Returns the maximum value in the sample data.
        /// </summary>
        /// <param name="data">The sample data.</param>
        /// <returns>The maximum value in the sample data.</returns>
        public static double Maximum(this IEnumerable<double?> data)
        {
            if (data == null)
            {
                throw new ArgumentNullException("data");
            }

            double max = double.MinValue;
            int count = 0;
            foreach (double? d in data)
            {
                if (d.HasValue)
                {
                    max = Math.Max(max, d.Value);
                    count++;
                }
            }

            if (count == 0)
            {
                throw new ArgumentException(Resources.CollectionEmpty, "data");
            }

            return max;
        }

        /// <summary>
        /// Returns the minimum value in the sample data.
        /// </summary>
        /// <param name="data">The sample data.</param>
        /// <returns>The minimum value in the sample data.</returns>
        public static double Minimum(this IEnumerable<double> data)
        {
            if (data == null)
            {
                throw new ArgumentNullException("data");
            }

            double min = double.MaxValue;
            int count = 0;
            foreach (double d in data)
            {
                min = Math.Min(min, d);
                count++;
            }

            if (count == 0)
            {
                throw new ArgumentException(Resources.CollectionEmpty, "data");
            }

            return min;
        }

        /// <summary>
        /// Returns the maximum value in the sample data.
        /// </summary>
        /// <param name="data">The sample data.</param>
        /// <returns>The maximum value in the sample data.</returns>
        public static double Maximum(this IEnumerable<double> data)
        {
            if (data == null)
            {
                throw new ArgumentNullException("data");
            }

            double max = double.MinValue;
            int count = 0;
            foreach (double d in data)
            {
                max = Math.Max(max, d);
                count++;
            }

            if (count == 0)
            {
                throw new ArgumentException(Resources.CollectionEmpty, "data");
            }

            return max;
        }

        /// <summary>
        /// Calculates the sample median.
        /// </summary>
        /// <param name="data">The data to calculate the median of.</param>
        /// <returns>The median of the sample.</returns>
        public static double Median(this IEnumerable<double> data)
        {
            if (data == null)
            {
                throw new ArgumentNullException("data");
            }

            var dataArray = new List<double>(data);
            int index = (dataArray.Count / 2) + 1;
            if (dataArray.Count % 2 == 0)
            {
                double lower = OrderSelect(dataArray, 0, dataArray.Count - 1, index - 1);
                double upper = OrderSelect(dataArray, 0, dataArray.Count - 1, index);
                return (lower + upper) / 2.0;
            }

            return OrderSelect(dataArray, 0, dataArray.Count - 1, index);
        }

        /// <summary>
        /// Calculates the sample median.
        /// </summary>
        /// <param name="data">The data to calculate the median of.</param>
        /// <returns>The median of the sample.</returns>
        public static double Median(this IEnumerable<double?> data)
        {
            if (data == null)
            {
                throw new ArgumentNullException("data");
            }

            var nonNull = new List<double>();
            foreach (double? value in data)
            {
                if (value.HasValue)
                {
                    nonNull.Add(value.Value);
                }
            }

            if (nonNull.Count == 0)
            {
                throw new ArgumentException(Resources.CollectionEmpty, "data");
            }

            return nonNull.Median();
        }

        /// <summary>
        /// Evaluate the i-order (1..N) statistic of the provided samples.
        /// </summary>
        /// <param name="samples">The sample data.</param>
        /// <param name="order">Order of the statistic to evaluate.</param>
        /// <returns>The i'th order statistic in the sample data.</returns>
        public static double OrderStatistic(IEnumerable<double> samples, int order)
        {
            if (order == 1)
            {
                // Can be done in linear time by Min()
                return Minimum(samples);
            }

            var list = new List<double>(samples);
            if (order < 1 || order > list.Count)
            {
                throw new ArgumentOutOfRangeException("order", Resources.ArgumentInIntervalXYInclusive);
            }

            if (order == list.Count)
            {
                // Can be done in linear time by Max()
                return Maximum(list);
            }

            return OrderSelect(list, 0, list.Count - 1, order);
        }

        /// <summary>
        /// Implementation of the order statistics finding algorithm based on the algorithm in
        /// "Introduction to Algorithms", Cormen et al. section 7.1.
        /// </summary>
        /// <param name="samples">The sample data.</param>
        /// <param name="left">The left bound in which to order select.</param>
        /// <param name="right">The right bound in which to order select.</param>
        /// <param name="order">The order we are trying to find.</param>
        /// <returns>The <paramref name="order"/> order statistic.</returns>
        private static double OrderSelect(IList<double> samples, int left, int right, int order)
        {
            System.Diagnostics.Debug.Assert(order > 0, "Order must always be positive.");
            System.Diagnostics.Debug.Assert(left >= 0 && left <= right, "Left side must always be positive and smaller than right side.");
            System.Diagnostics.Debug.Assert(right < samples.Count, "Right side must always be smaller than number of elements in list.");
            System.Diagnostics.Debug.Assert(right - left + 1 >= order, "Make sure there are at least order items in the segment [left, right].");

            if (left == right)
            {
                return samples[left];
            }

            // The pivot point.
            double pivot = samples[right];

            // The partioning code.
            int i = left - 1;
            for (int j = left; j <= right - 1; j++)
            {
                if (samples[j] <= pivot)
                {
                    i++;
                    Sorting.Swap(samples, i, j);
                }
            }

            Sorting.Swap(samples, i + 1, right);

            // Recursive order finding algorithm.
            if (order == (i - left) + 2)
            {
                return pivot;
            }

            if (order < (i - left) + 2)
            {
                return OrderSelect(samples, left, i, order);
            }

            return OrderSelect(samples, i + 2, right, order - i + left - 2);
        }
    }
}