Module: DaruLite::Maths::Statistics::Vector
- Extended by:
- Gem::Deprecate
- Included in:
- Vector
- Defined in:
- lib/daru_lite/maths/statistics/vector.rb
Overview
rubocop:disable Metrics/ModuleLength
Instance Method Summary collapse
-
#acf(max_lags = nil) ⇒ Object
Calculates the autocorrelation coefficients of the series.
-
#acvf(demean = true, unbiased = true) ⇒ Object
Provides autocovariance.
- #average_deviation_population(m = nil) ⇒ Object (also: #adp)
-
#box_cox_transformation(lambda) ⇒ Object
:nocov:.
-
#center ⇒ Object
Center data by subtracting the mean from each non-nil value.
- #coefficient_of_variation ⇒ Object (also: #cov)
-
#count(value = false, &block) ⇒ Object
Retrieves number of cases which comply condition.
-
#covariance_population(other) ⇒ Object
Population covariance with denominator (N).
-
#covariance_sample(other) ⇒ Object
(also: #covariance)
Sample covariance with denominator (N-1).
-
#cumsum ⇒ Object
Calculate cumulative sum of Vector.
-
#describe(methods = nil) ⇒ Object
Create a summary of count, mean, standard deviation, min and max of the vector in one shot.
-
#dichotomize(low = nil) ⇒ Object
Dichotomize the vector with 0 and 1, based on lowest value.
-
#diff(max_lags = 1) ⇒ DaruLite::Vector
Performs the difference of the series.
-
#ema(n = 10, wilder = false) ⇒ DaruLite::Vector
Exponential Moving Average.
-
#emsd(n = 10, wilder = false) ⇒ DaruLite::Vector
Exponential Moving Standard Deviation.
-
#emv(n = 10, wilder = false) ⇒ DaruLite::Vector
Exponential Moving Variance.
-
#factors ⇒ Object
Retrieve unique values of non-nil data.
- #frequencies ⇒ Object (also: #freqs)
-
#index_of_max(size = nil, &block) ⇒ Object
Returns the index of the maximum value(s) present in the vector, with an optional comparator block.
-
#index_of_max_by(size = nil, &block) ⇒ Object
Returns the index of the maximum value(s) present in the vector, with a compulsory object block.
-
#index_of_min(size = nil, &block) ⇒ Object
Returns the index of the minimum value(s) present in the vector, with an optional comparator block.
-
#index_of_min_by(size = nil, &block) ⇒ Object
Returns the index of the minimum value(s) present in the vector, with a compulsory object block.
- #kurtosis(m = nil) ⇒ Object
-
#macd(fast = 12, slow = 26, signal = 9) ⇒ Array<DaruLite::Vector>
Moving Average Convergence-Divergence.
-
#max(size = nil, &block) ⇒ Object
Returns the maximum value(s) present in the vector, with an optional comparator block.
-
#max_by(size = nil, &block) ⇒ Object
Returns the maximum value(s) present in the vector, with a compulsory object block.
-
#max_index ⇒ DaruLite::Vector
Return the maximum element present in the Vector, as a Vector.
- #mean ⇒ Object
- #median ⇒ Object
- #median_absolute_deviation ⇒ Object (also: #mad)
-
#min(size = nil, &block) ⇒ Object
Returns the minimum value(s) present in the vector, with an optional comparator block.
-
#min_by(size = nil, &block) ⇒ Object
Returns the minimum value(s) present in the vector, with a compulsory object block.
- #mode ⇒ Object
-
#percent_change(periods = 1) ⇒ Object
The percent_change method computes the percent change over the given number of periods.
-
#percentile(q, strategy = :midpoint) ⇒ Object
(also: #percentil)
Returns the value of the percentile q.
- #product ⇒ Object
- #proportion(value = 1) ⇒ Object
- #proportions ⇒ Object
- #range ⇒ Object
- #ranked ⇒ Object
-
#rolling(function, n = 10) ⇒ DaruLite::Vector
Calculate the rolling function for a loopback value.
-
#rolling_count {|n| ... } ⇒ Object
Calculate rolling non-missing count.
-
#rolling_max {|n| ... } ⇒ Object
Calculate rolling max value.
-
#rolling_mean {|n| ... } ⇒ Object
Calculate rolling average.
-
#rolling_median {|n| ... } ⇒ Object
Calculate rolling median.
-
#rolling_min {|n| ... } ⇒ Object
Calculate rolling min value.
-
#rolling_std {|n| ... } ⇒ Object
Calculate rolling standard deviation.
-
#rolling_sum {|n| ... } ⇒ Object
Calculate rolling sum.
-
#rolling_variance {|n| ... } ⇒ Object
Calculate rolling variance.
-
#sample_with_replacement(sample = 1) ⇒ Object
Returns an random sample of size n, with replacement, only with non-nil data.
-
#sample_without_replacement(sample = 1) ⇒ Object
Returns an random sample of size n, without replacement, only with valid data.
-
#skew(m = nil) ⇒ Object
Calculate skewness using (sigma(xi - mean)^3)/((N)*std_dev_sample^3).
- #standard_deviation_population(m = nil) ⇒ Object (also: #sdp)
- #standard_deviation_sample(m = nil) ⇒ Object (also: #sds, #sd)
- #standard_error ⇒ Object (also: #se)
-
#standardize(use_population = false) ⇒ Object
Standardize data.
- #sum ⇒ Object
- #sum_of_squared_deviation ⇒ Object
- #sum_of_squares(m = nil) ⇒ Object (also: #ss)
-
#value_counts ⇒ Object
Count number of occurrences of each value in the Vector.
-
#variance_population(m = nil) ⇒ Object
Population variance with denominator (N).
-
#variance_sample(m = nil) ⇒ Object
(also: #variance)
Sample variance with denominator (N-1).
- #vector_centered_compute(m) ⇒ Object
-
#vector_percentile ⇒ Object
Replace each non-nil value in the vector with its percentile.
- #vector_standardized_compute(m, sd) ⇒ Object
Instance Method Details
#acf(max_lags = nil) ⇒ Object
Calculates the autocorrelation coefficients of the series.
The first element is always 1, since that is the correlation of the series with itself.
887 888 889 890 891 892 893 894 895 896 897 898 899 900 |
# File 'lib/daru_lite/maths/statistics/vector.rb', line 887 def acf(max_lags = nil) max_lags ||= (10 * Math.log10(size)).to_i (0..max_lags).map do |i| if i.zero? 1.0 else m = mean # can't use Pearson coefficient since the mean for the lagged series should # be the same as the regular series ((self - m) * (lag(i) - m)).sum / variance_sample / (size - 1) end end end |
#acvf(demean = true, unbiased = true) ⇒ Object
Provides autocovariance.
Options
-
:demean = true; optional. Supply false if series is not to be demeaned
-
:unbiased = true; optional. true/false for unbiased/biased form of autocovariance
Returns
Autocovariance value
912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 |
# File 'lib/daru_lite/maths/statistics/vector.rb', line 912 def acvf(demean = true, unbiased = true) demeaned_series = demean ? self - mean : self n = (10 * Math.log10(size)).to_i + 1 m = mean d = if unbiased Array.new(size, size) else (1..size).to_a.reverse[0..n] end 0.upto(n - 1).map do |i| (demeaned_series * (lag(i) - m)).sum / d[i] end end |
#average_deviation_population(m = nil) ⇒ Object Also known as: adp
501 502 503 504 505 506 507 |
# File 'lib/daru_lite/maths/statistics/vector.rb', line 501 def average_deviation_population(m = nil) must_be_numeric! m ||= mean reject_values(*DaruLite::MISSING_VALUES).data.inject(0) do |memo, val| (val - m).abs + memo end.quo(size - count_values(*DaruLite::MISSING_VALUES)) end |
#box_cox_transformation(lambda) ⇒ Object
:nocov:
567 568 569 570 571 572 573 574 575 576 577 578 579 |
# File 'lib/daru_lite/maths/statistics/vector.rb', line 567 def box_cox_transformation(lambda) # :nodoc: must_be_numeric! recode do |x| unless x.nil? if lambda.zero? Math.log(x) else ((x**lambda) - 1).quo(lambda) end end end end |
#center ⇒ Object
Center data by subtracting the mean from each non-nil value.
548 549 550 |
# File 'lib/daru_lite/maths/statistics/vector.rb', line 548 def center self - mean end |
#coefficient_of_variation ⇒ Object Also known as: cov
392 393 394 |
# File 'lib/daru_lite/maths/statistics/vector.rb', line 392 def coefficient_of_variation standard_deviation_sample / mean end |
#count(value = false, &block) ⇒ Object
Retrieves number of cases which comply condition. If block given, retrieves number of instances where block returns true. If other values given, retrieves the frequency for this value. If no value given, counts the number of non-nil elements in the Vector.
400 401 402 403 404 405 406 407 408 |
# File 'lib/daru_lite/maths/statistics/vector.rb', line 400 def count(value = false, &block) if block @data.count(&block) elsif value count { |val| val == value } else size - indexes(*DaruLite::MISSING_VALUES).size end end |
#covariance_population(other) ⇒ Object
Population covariance with denominator (N)
450 451 452 453 |
# File 'lib/daru_lite/maths/statistics/vector.rb', line 450 def covariance_population(other) size == other.size or raise ArgumentError, 'size of both the vectors must be equal' covariance_sum(other) / (size - count_values(*DaruLite::MISSING_VALUES)) end |
#covariance_sample(other) ⇒ Object Also known as: covariance
Sample covariance with denominator (N-1)
444 445 446 447 |
# File 'lib/daru_lite/maths/statistics/vector.rb', line 444 def covariance_sample(other) size == other.size or raise ArgumentError, 'size of both the vectors must be equal' covariance_sum(other) / (size - count_values(*DaruLite::MISSING_VALUES) - 1) end |
#cumsum ⇒ Object
Calculate cumulative sum of Vector
929 930 931 932 933 934 935 936 937 938 939 940 941 942 |
# File 'lib/daru_lite/maths/statistics/vector.rb', line 929 def cumsum result = [] acc = 0 @data.each do |d| if include_with_nan? DaruLite::MISSING_VALUES, d result << nil else acc += d result << acc end end DaruLite::Vector.new(result, index: @index) end |
#describe(methods = nil) ⇒ Object
Create a summary of count, mean, standard deviation, min and max of the vector in one shot.
Arguments
methods
- An array with aggregation methods specified as symbols to be applied to vectors. Default is [:count, :mean, :std, :max, :min]. Methods will be applied in the specified order.
43 44 45 46 47 |
# File 'lib/daru_lite/maths/statistics/vector.rb', line 43 def describe(methods = nil) methods ||= %i[count mean std min max] description = methods.map { |m| send(m) } DaruLite::Vector.new(description, index: methods, name: :statistics) end |
#dichotomize(low = nil) ⇒ Object
Dichotomize the vector with 0 and 1, based on lowest value. If parameter is defined, this value and lower will be 0 and higher, 1.
533 534 535 536 537 538 539 540 541 542 543 544 545 |
# File 'lib/daru_lite/maths/statistics/vector.rb', line 533 def dichotomize(low = nil) low ||= factors.min recode do |x| if x.nil? nil elsif x > low 1 else 0 end end end |
#diff(max_lags = 1) ⇒ DaruLite::Vector
Performs the difference of the series. Note: The first difference of series is X(t) - X(t-1) But, second difference of series is NOT X(t) - X(t-2) It is the first difference of the first difference
> (X(t) - X(t-1)) - (X(t-1) - X(t-2))
Arguments
-
max_lags: integer, (default: 1), number of differences reqd.
687 688 689 690 691 692 693 694 695 |
# File 'lib/daru_lite/maths/statistics/vector.rb', line 687 def diff(max_lags = 1) ts = self difference = [] max_lags.times do difference = ts - ts.lag ts = difference end difference end |
#ema(n = 10, wilder = false) ⇒ DaruLite::Vector
Exponential Moving Average. Calculates an exponential moving average of the series using a specified parameter. If wilder is false (the default) then the EMA uses a smoothing value of 2 / (n + 1), if it is true then it uses the Welles Wilder smoother of 1 / n.
Warning for EMA usage: EMAs are unstable for small series, as they use a lot more than n observations to calculate. The series is stable if the size of the series is >= 3.45 * (n + 1)
771 772 773 774 775 776 777 778 779 780 781 782 783 784 |
# File 'lib/daru_lite/maths/statistics/vector.rb', line 771 def ema(n = 10, wilder = false) smoother = wilder ? 1.0 / n : 2.0 / (n + 1) # need to start everything from the first non-nil observation start = @data.index { |i| !i.nil? } # first n - 1 observations are nil base = [nil] * (start + n - 1) # nth observation is just a moving average base << (@data[start...(start + n)].inject(0.0) { |s, a| a.nil? ? s : s + a } / n) (start + n).upto size - 1 do |i| base << ((self[i] * smoother) + ((1 - smoother) * base.last)) end DaruLite::Vector.new(base, index: @index, name: @name) end |
#emsd(n = 10, wilder = false) ⇒ DaruLite::Vector
Exponential Moving Standard Deviation. Calculates an exponential moving standard deviation of the series using a specified parameter. If wilder is false (the default) then the EMSD uses a smoothing value of 2 / (n + 1), if it is true then it uses the Welles Wilder smoother of 1 / n.
842 843 844 845 846 847 848 849 |
# File 'lib/daru_lite/maths/statistics/vector.rb', line 842 def emsd(n = 10, wilder = false) result = [] emv_return = emv(n, wilder) emv_return.each do |d| result << (d.nil? ? nil : Math.sqrt(d)) end DaruLite::Vector.new(result, index: @index, name: @name) end |
#emv(n = 10, wilder = false) ⇒ DaruLite::Vector
Exponential Moving Variance. Calculates an exponential moving variance of the series using a specified parameter. If wilder is false (the default) then the EMV uses a smoothing value of 2 / (n + 1), if it is true then it uses the Welles Wilder smoother of 1 / n.
805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 |
# File 'lib/daru_lite/maths/statistics/vector.rb', line 805 def emv(n = 10, wilder = false) # rubocop:disable Metrics/AbcSize smoother = wilder ? 1.0 / n : 2.0 / (n + 1) # need to start everything from the first non-nil observation start = @data.index { |i| !i.nil? } # first n - 1 observations are nil var_base = [nil] * (start + n - 1) mean_base = [nil] * (start + n - 1) mean_base << (@data[start...(start + n)].inject(0.0) { |s, a| a.nil? ? s : s + a } / n) # nth observation is just a moving variance_population var_base << (@data[start...(start + n)].inject(0.0) { |s, x| x.nil? ? s : s + ((x - mean_base.last)**2) } / n) (start + n).upto size - 1 do |i| last = mean_base.last mean_base << ((self[i] * smoother) + ((1 - smoother) * last)) var_base << (((1 - smoother) * var_base.last) + (smoother * (self[i] - last) * (self[i] - mean_base.last))) end DaruLite::Vector.new(var_base, index: @index, name: @name) end |
#factors ⇒ Object
Retrieve unique values of non-nil data
68 69 70 |
# File 'lib/daru_lite/maths/statistics/vector.rb', line 68 def factors reject_values(*DaruLite::MISSING_VALUES).uniq.reset_index! end |
#frequencies ⇒ Object Also known as: freqs
364 365 366 367 368 369 370 |
# File 'lib/daru_lite/maths/statistics/vector.rb', line 364 def frequencies DaruLite::Vector.new( @data.each_with_object(Hash.new(0)) do |element, hash| hash[element] += 1 unless element.nil? end ) end |
#index_of_max(size = nil, &block) ⇒ Object
Returns the index of the maximum value(s) present in the vector, with an optional comparator block.
279 280 281 282 283 |
# File 'lib/daru_lite/maths/statistics/vector.rb', line 279 def index_of_max(size = nil, &block) vals = max(size, &block) dv = reject_values(*DaruLite::MISSING_VALUES) vals.is_a?(Array) ? (vals.map { |x| dv.index_of(x) }) : dv.index_of(vals) end |
#index_of_max_by(size = nil, &block) ⇒ Object
Returns the index of the maximum value(s) present in the vector, with a compulsory object block.
302 303 304 305 306 |
# File 'lib/daru_lite/maths/statistics/vector.rb', line 302 def index_of_max_by(size = nil, &block) vals = max_by(size, &block) dv = reject_values(*DaruLite::MISSING_VALUES) vals.is_a?(Array) ? (vals.map { |x| dv.index_of(x) }) : dv.index_of(vals) end |
#index_of_min(size = nil, &block) ⇒ Object
Returns the index of the minimum value(s) present in the vector, with an optional comparator block.
328 329 330 331 332 |
# File 'lib/daru_lite/maths/statistics/vector.rb', line 328 def index_of_min(size = nil, &block) vals = min(size, &block) dv = reject_values(*DaruLite::MISSING_VALUES) vals.is_a?(Array) ? (vals.map { |x| dv.index_of(x) }) : dv.index_of(vals) end |
#index_of_min_by(size = nil, &block) ⇒ Object
Returns the index of the minimum value(s) present in the vector, with a compulsory object block.
351 352 353 354 355 |
# File 'lib/daru_lite/maths/statistics/vector.rb', line 351 def index_of_min_by(size = nil, &block) vals = min_by(size, &block) dv = reject_values(*DaruLite::MISSING_VALUES) vals.is_a?(Array) ? (vals.map { |x| dv.index_of(x) }) : dv.index_of(vals) end |
#kurtosis(m = nil) ⇒ Object
491 492 493 494 495 496 497 498 499 |
# File 'lib/daru_lite/maths/statistics/vector.rb', line 491 def kurtosis(m = nil) if @data.respond_to? :kurtosis @data.kurtosis else m ||= mean fo = @data.inject(0) { |a, x| a + ((x - m)**4) } fo.quo((size - indexes(*DaruLite::MISSING_VALUES).size) * (standard_deviation_sample(m)**4)) - 3 end end |
#macd(fast = 12, slow = 26, signal = 9) ⇒ Array<DaruLite::Vector>
Moving Average Convergence-Divergence. Calculates the MACD (moving average convergence-divergence) of the time series.
870 871 872 873 874 875 |
# File 'lib/daru_lite/maths/statistics/vector.rb', line 870 def macd(fast = 12, slow = 26, signal = 9) macdseries = ema(fast) - ema(slow) macdsignal = macdseries.ema(signal) macdhist = macdseries - macdsignal [macdseries, macdsignal, macdhist] end |
#max(size = nil, &block) ⇒ Object
Returns the maximum value(s) present in the vector, with an optional comparator block.
92 93 94 |
# File 'lib/daru_lite/maths/statistics/vector.rb', line 92 def max(size = nil, &block) reject_values(*DaruLite::MISSING_VALUES).to_a.max(size, &block) end |
#max_by(size = nil, &block) ⇒ Object
Returns the maximum value(s) present in the vector, with a compulsory object block.
112 113 114 115 116 |
# File 'lib/daru_lite/maths/statistics/vector.rb', line 112 def max_by(size = nil, &block) raise ArgumentError, 'Expected compulsory object block in max_by method' unless block reject_values(*DaruLite::MISSING_VALUES).to_a.max_by(size, &block) end |
#max_index ⇒ DaruLite::Vector
Return the maximum element present in the Vector, as a Vector.
359 360 361 362 |
# File 'lib/daru_lite/maths/statistics/vector.rb', line 359 def max_index max_value = @data.max DaruLite::Vector.new({ index_of(max_value) => max_value }, name: @name, dtype: @dtype) end |
#mean ⇒ Object
10 11 12 |
# File 'lib/daru_lite/maths/statistics/vector.rb', line 10 def mean @data.mean end |
#median ⇒ Object
26 27 28 |
# File 'lib/daru_lite/maths/statistics/vector.rb', line 26 def median @data.respond_to?(:median) ? @data.median : percentile(50) end |
#median_absolute_deviation ⇒ Object Also known as: mad
49 50 51 52 |
# File 'lib/daru_lite/maths/statistics/vector.rb', line 49 def median_absolute_deviation m = median recode { |val| (val - m).abs }.median end |
#min(size = nil, &block) ⇒ Object
Returns the minimum value(s) present in the vector, with an optional comparator block.
137 138 139 |
# File 'lib/daru_lite/maths/statistics/vector.rb', line 137 def min(size = nil, &block) reject_values(*DaruLite::MISSING_VALUES).to_a.min(size, &block) end |
#min_by(size = nil, &block) ⇒ Object
Returns the minimum value(s) present in the vector, with a compulsory object block.
157 158 159 160 161 |
# File 'lib/daru_lite/maths/statistics/vector.rb', line 157 def min_by(size = nil, &block) raise ArgumentError, 'Expected compulsory object block in min_by method' unless block reject_values(*DaruLite::MISSING_VALUES).to_a.min_by(size, &block) end |
#mode ⇒ Object
30 31 32 33 |
# File 'lib/daru_lite/maths/statistics/vector.rb', line 30 def mode mode = frequencies.to_h.select { |_, v| v == frequencies.max }.keys mode.size > 1 ? DaruLite::Vector.new(mode) : mode.first end |
#percent_change(periods = 1) ⇒ Object
The percent_change method computes the percent change over the given number of periods.
652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 |
# File 'lib/daru_lite/maths/statistics/vector.rb', line 652 def percent_change(periods = 1) must_be_numeric! prev = nil arr = @data.each_with_index.map do |cur, i| if i < periods || include_with_nan?(DaruLite::MISSING_VALUES, cur) || include_with_nan?(DaruLite::MISSING_VALUES, prev) nil else (cur - prev) / prev.to_f end.tap { prev = cur if cur } end DaruLite::Vector.new(arr, index: @index, name: @name) end |
#percentile(q, strategy = :midpoint) ⇒ Object Also known as: percentil
Returns the value of the percentile q
Accepts an optional second argument specifying the strategy to interpolate when the requested percentile lies between two data points a and b Valid strategies are:
-
:midpoint (Default): (a + b) / 2
-
:linear : a + (b - a) * d where d is the decimal part of the index between a and b.
References
This is the NIST recommended method (en.wikipedia.org/wiki/Percentile#NIST_method)
519 520 521 522 523 524 525 526 527 528 |
# File 'lib/daru_lite/maths/statistics/vector.rb', line 519 def percentile(q, strategy = :midpoint) case strategy when :midpoint midpoint_percentile(q) when :linear linear_percentile(q) else raise ArgumentError, "Unknown strategy #{strategy}" end end |
#product ⇒ Object
18 19 20 |
# File 'lib/daru_lite/maths/statistics/vector.rb', line 18 def product @data.product end |
#proportion(value = 1) ⇒ Object
419 420 421 |
# File 'lib/daru_lite/maths/statistics/vector.rb', line 419 def proportion(value = 1) frequencies[value].quo(size - count_values(*DaruLite::MISSING_VALUES)).to_f end |
#proportions ⇒ Object
375 376 377 378 379 380 |
# File 'lib/daru_lite/maths/statistics/vector.rb', line 375 def proportions len = size - count_values(*DaruLite::MISSING_VALUES) frequencies.to_h.transform_values do |count| count / len.to_f end end |
#range ⇒ Object
22 23 24 |
# File 'lib/daru_lite/maths/statistics/vector.rb', line 22 def range max - min end |
#ranked ⇒ Object
382 383 384 385 386 387 388 389 390 |
# File 'lib/daru_lite/maths/statistics/vector.rb', line 382 def ranked sum = 0 r = frequencies.to_h.sort.each_with_object({}) do |(el, count), memo| memo[el] = ((sum + 1) + (sum + count)).quo(2) sum += count end recode { |e| r[e] } end |
#rolling(function, n = 10) ⇒ DaruLite::Vector
Calculate the rolling function for a loopback value.
709 710 711 712 713 714 715 716 |
# File 'lib/daru_lite/maths/statistics/vector.rb', line 709 def rolling(function, n = 10) DaruLite::Vector.new( ([nil] * (n - 1)) + (0..(size - n)).map do |i| DaruLite::Vector.new(@data[i...(i + n)]).send(function) end, index: @index ) end |
#rolling_count {|n| ... } ⇒ Object
Calculate rolling non-missing count
742 743 744 745 746 |
# File 'lib/daru_lite/maths/statistics/vector.rb', line 742 %i[count mean median max min sum std variance].each do |meth| define_method(:"rolling_#{meth}") do |n = 10| rolling(meth, n) end end |
#rolling_max {|n| ... } ⇒ Object
Calculate rolling max value
742 743 744 745 746 |
# File 'lib/daru_lite/maths/statistics/vector.rb', line 742 %i[count mean median max min sum std variance].each do |meth| define_method(:"rolling_#{meth}") do |n = 10| rolling(meth, n) end end |
#rolling_mean {|n| ... } ⇒ Object
Calculate rolling average
742 743 744 745 746 |
# File 'lib/daru_lite/maths/statistics/vector.rb', line 742 %i[count mean median max min sum std variance].each do |meth| define_method(:"rolling_#{meth}") do |n = 10| rolling(meth, n) end end |
#rolling_median {|n| ... } ⇒ Object
Calculate rolling median
742 743 744 745 746 |
# File 'lib/daru_lite/maths/statistics/vector.rb', line 742 %i[count mean median max min sum std variance].each do |meth| define_method(:"rolling_#{meth}") do |n = 10| rolling(meth, n) end end |
#rolling_min {|n| ... } ⇒ Object
Calculate rolling min value
742 743 744 745 746 |
# File 'lib/daru_lite/maths/statistics/vector.rb', line 742 %i[count mean median max min sum std variance].each do |meth| define_method(:"rolling_#{meth}") do |n = 10| rolling(meth, n) end end |
#rolling_std {|n| ... } ⇒ Object
Calculate rolling standard deviation
742 743 744 745 746 |
# File 'lib/daru_lite/maths/statistics/vector.rb', line 742 %i[count mean median max min sum std variance].each do |meth| define_method(:"rolling_#{meth}") do |n = 10| rolling(meth, n) end end |
#rolling_sum {|n| ... } ⇒ Object
Calculate rolling sum
742 743 744 745 746 |
# File 'lib/daru_lite/maths/statistics/vector.rb', line 742 %i[count mean median max min sum std variance].each do |meth| define_method(:"rolling_#{meth}") do |n = 10| rolling(meth, n) end end |
#rolling_variance {|n| ... } ⇒ Object
Calculate rolling variance
742 743 744 745 746 |
# File 'lib/daru_lite/maths/statistics/vector.rb', line 742 %i[count mean median max min sum std variance].each do |meth| define_method(:"rolling_#{meth}") do |n = 10| rolling(meth, n) end end |
#sample_with_replacement(sample = 1) ⇒ Object
Returns an random sample of size n, with replacement, only with non-nil data.
In all the trails, every item have the same probability of been selected.
611 612 613 614 615 616 617 618 619 |
# File 'lib/daru_lite/maths/statistics/vector.rb', line 611 def sample_with_replacement(sample = 1) if @data.respond_to? :sample_with_replacement @data.sample_with_replacement sample else valid = indexes(*DaruLite::MISSING_VALUES).empty? ? self : reject_values(*DaruLite::MISSING_VALUES) vds = valid.size (0...sample).collect { valid[rand(vds)] } end end |
#sample_without_replacement(sample = 1) ⇒ Object
Returns an random sample of size n, without replacement, only with valid data.
Every element could only be selected once.
A sample of the same size of the vector is the vector itself.
627 628 629 630 631 632 633 |
# File 'lib/daru_lite/maths/statistics/vector.rb', line 627 def sample_without_replacement(sample = 1) if @data.respond_to? :sample_without_replacement @data.sample_without_replacement sample else raw_sample_without_replacement(sample) end end |
#skew(m = nil) ⇒ Object
Calculate skewness using (sigma(xi - mean)^3)/((N)*std_dev_sample^3)
481 482 483 484 485 486 487 488 489 |
# File 'lib/daru_lite/maths/statistics/vector.rb', line 481 def skew(m = nil) if @data.respond_to? :skew @data.skew else m ||= mean th = @data.inject(0) { |memo, val| memo + ((val - m)**3) } th.quo((size - indexes(*DaruLite::MISSING_VALUES).size) * (standard_deviation_sample(m)**3)) end end |
#standard_deviation_population(m = nil) ⇒ Object Also known as: sdp
462 463 464 465 466 467 468 469 |
# File 'lib/daru_lite/maths/statistics/vector.rb', line 462 def standard_deviation_population(m = nil) m ||= mean if @data.respond_to? :standard_deviation_population @data.standard_deviation_population(m) else Math.sqrt(variance_population(m)) end end |
#standard_deviation_sample(m = nil) ⇒ Object Also known as: sds, sd
471 472 473 474 475 476 477 478 |
# File 'lib/daru_lite/maths/statistics/vector.rb', line 471 def standard_deviation_sample(m = nil) m ||= mean if @data.respond_to? :standard_deviation_sample @data.standard_deviation_sample m else Math.sqrt(variance_sample(m)) end end |
#standard_error ⇒ Object Also known as: se
56 57 58 |
# File 'lib/daru_lite/maths/statistics/vector.rb', line 56 def standard_error standard_deviation_sample / Math.sqrt(size - count_values(*DaruLite::MISSING_VALUES)) end |
#standardize(use_population = false) ⇒ Object
Standardize data.
Arguments
-
use_population - Pass as true if you want to use population
standard deviation instead of sample standard deviation.
558 559 560 561 562 563 564 |
# File 'lib/daru_lite/maths/statistics/vector.rb', line 558 def standardize(use_population = false) m ||= mean sd = use_population ? sdp : sds return DaruLite::Vector.new([nil] * size) if m.nil? || sd.to_d == BigDecimal('0.0') vector_standardized_compute m, sd end |
#sum ⇒ Object
14 15 16 |
# File 'lib/daru_lite/maths/statistics/vector.rb', line 14 def sum @data.sum end |
#sum_of_squared_deviation ⇒ Object
60 61 62 63 64 65 |
# File 'lib/daru_lite/maths/statistics/vector.rb', line 60 def sum_of_squared_deviation ( @data.inject(0) { |a, x| (x**2) + a } - (sum**2).quo(size - count_values(*DaruLite::MISSING_VALUES)).to_f ).to_f end |
#sum_of_squares(m = nil) ⇒ Object Also known as: ss
455 456 457 458 459 460 |
# File 'lib/daru_lite/maths/statistics/vector.rb', line 455 def sum_of_squares(m = nil) m ||= mean reject_values(*DaruLite::MISSING_VALUES).data.inject(0) do |memo, val| memo + ((val - m)**2) end end |
#value_counts ⇒ Object
Count number of occurrences of each value in the Vector
411 412 413 414 415 416 417 |
# File 'lib/daru_lite/maths/statistics/vector.rb', line 411 def value_counts values = @data.each_with_object(Hash.new(0)) do |d, memo| memo[d] += 1 end DaruLite::Vector.new(values) end |
#variance_population(m = nil) ⇒ Object
Population variance with denominator (N)
434 435 436 437 438 439 440 441 |
# File 'lib/daru_lite/maths/statistics/vector.rb', line 434 def variance_population(m = nil) m ||= mean if @data.respond_to? :variance_population @data.variance_population m else sum_of_squares(m).quo(size - count_values(*DaruLite::MISSING_VALUES)).to_f end end |
#variance_sample(m = nil) ⇒ Object Also known as: variance
Sample variance with denominator (N-1)
424 425 426 427 428 429 430 431 |
# File 'lib/daru_lite/maths/statistics/vector.rb', line 424 def variance_sample(m = nil) m ||= mean if @data.respond_to? :variance_sample @data.variance_sample m else sum_of_squares(m).quo(size - count_values(*DaruLite::MISSING_VALUES) - 1) end end |
#vector_centered_compute(m) ⇒ Object
597 598 599 600 601 602 603 604 |
# File 'lib/daru_lite/maths/statistics/vector.rb', line 597 def vector_centered_compute(m) if @data.respond_to? :vector_centered_compute @data.vector_centered_compute(m) else DaruLite::Vector.new @data.collect { |x| x.nil? ? nil : x.to_f - m }, index: index, name: name, dtype: dtype end end |
#vector_percentile ⇒ Object
Replace each non-nil value in the vector with its percentile.
583 584 585 586 |
# File 'lib/daru_lite/maths/statistics/vector.rb', line 583 def vector_percentile c = size - indexes(*DaruLite::MISSING_VALUES).size ranked.recode! { |i| i.nil? ? nil : (i.quo(c) * 100).to_f } end |
#vector_standardized_compute(m, sd) ⇒ Object
588 589 590 591 592 593 594 595 |
# File 'lib/daru_lite/maths/statistics/vector.rb', line 588 def vector_standardized_compute(m, sd) if @data.respond_to? :vector_standardized_compute @data.vector_standardized_compute(m, sd) else DaruLite::Vector.new @data.collect { |x| x.nil? ? nil : (x.to_f - m).quo(sd) }, index: index, name: name, dtype: dtype end end |