Module: Daru::Maths::Statistics::Vector
- Included in:
- Vector
- Defined in:
- lib/daru/maths/statistics/vector.rb
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
:nodoc:.
-
#center ⇒ Object
Center data by subtracting the mean from each non-nil value.
- #coefficient_of_variation ⇒ Object (also: #cov)
-
#count(value = false) ⇒ 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) ⇒ Daru::Vector
Performs the difference of the series.
-
#ema(n = 10, wilder = false) ⇒ Daru::Vector
Exponential Moving Average.
-
#emsd(n = 10, wilder = false) ⇒ Daru::Vector
Exponential Moving Standard Deviation.
-
#emv(n = 10, wilder = false) ⇒ Daru::Vector
Exponential Moving Variance.
-
#factors ⇒ Object
Retrieve unique values of non-nil data.
- #freqs ⇒ Object
- #frequencies ⇒ Object
- #kurtosis(m = nil) ⇒ Object
-
#macd(fast = 12, slow = 26, signal = 9) ⇒ Object
Moving Average Convergence-Divergence.
-
#max(return_type = :stored_type) ⇒ Object
Maximum element of the vector.
-
#max_index ⇒ Daru::Vector
Return a Vector with the max element and its index.
- #mean ⇒ Object
- #median ⇒ Object
- #median_absolute_deviation ⇒ Object (also: #mad)
- #min ⇒ Object
- #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) ⇒ Daru::Vector
Calculate the rolling function for a loopback value.
-
#rolling_count ⇒ Object
Calculate rolling non-missing count.
-
#rolling_max ⇒ Object
Calculate rolling max value.
-
#rolling_mean ⇒ Object
Calculate rolling average.
-
#rolling_median ⇒ Object
Calculate rolling median.
-
#rolling_min ⇒ Object
Calculate rolling min value.
-
#rolling_std ⇒ Object
Calculate rolling standard deviation.
-
#rolling_sum ⇒ Object
Calculate rolling sum.
-
#rolling_variance ⇒ 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.
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# File 'lib/daru/maths/statistics/vector.rb', line 650 def acf(max_lags=nil) max_lags ||= (10 * Math.log10(size)).to_i (0..max_lags).map do |i| if i == 0 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
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# File 'lib/daru/maths/statistics/vector.rb', line 675 def acvf(demean=true, unbiased=true) opts = { demean: true, unbaised: true }.merge(opts) demean = opts[:demean] unbiased = opts[:unbiased] 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
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# File 'lib/daru/maths/statistics/vector.rb', line 238 def average_deviation_population m=nil type == :numeric or raise TypeError, 'Vector must be numeric' m ||= mean (@data.inject(0) { |memo, val| @missing_values.key?(val) ? memo : (val - m).abs + memo }).quo(n_valid) end |
#box_cox_transformation(lambda) ⇒ Object
:nodoc:
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# File 'lib/daru/maths/statistics/vector.rb', line 321 def box_cox_transformation lambda # :nodoc: raise 'Should be a numeric' unless @type == :numeric recode do |x| if !x.nil? if lambda == 0 Math.log(x) else (x ** lambda - 1).quo(lambda) end else nil end end end |
#center ⇒ Object
Center data by subtracting the mean from each non-nil value.
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# File 'lib/daru/maths/statistics/vector.rb', line 303 def center self - mean end |
#coefficient_of_variation ⇒ Object Also known as: cov
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# File 'lib/daru/maths/statistics/vector.rb', line 115 def coefficient_of_variation standard_deviation_sample / mean end |
#count(value = false) ⇒ 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.
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# File 'lib/daru/maths/statistics/vector.rb', line 123 def count value=false if block_given? @data.select { |val| yield(val) }.count elsif value val = frequencies[value] val.nil? ? 0 : val else size - @missing_positions.size end end |
#covariance_population(other) ⇒ Object
Population covariance with denominator (N)
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# File 'lib/daru/maths/statistics/vector.rb', line 181 def covariance_population other @size == other.size or raise ArgumentError, 'size of both the vectors must be equal' mean_x = mean mean_y = other.mean sum = 0 (0...size).each do |i| sum += ((@missing_values.key?(@data[i]) || other.missing_values.include?(other[i])) ? 0 : (@data[i] - mean_x) * (other.data[i] - mean_y)) end sum / n_valid end |
#covariance_sample(other) ⇒ Object Also known as: covariance
Sample covariance with denominator (N-1)
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# File 'lib/daru/maths/statistics/vector.rb', line 169 def covariance_sample other @size == other.size or raise ArgumentError, 'size of both the vectors must be equal' mean_x = mean mean_y = other.mean sum = 0 (0...size).each do |i| sum += ((@missing_values.key?(@data[i]) || other.missing_values.include?(other[i])) ? 0 : (@data[i] - mean_x) * (other.data[i] - mean_y)) end sum / (n_valid - 1) end |
#cumsum ⇒ Object
Calculate cumulative sum of Vector
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# File 'lib/daru/maths/statistics/vector.rb', line 699 def cumsum result = [] acc = 0 @data.each do |d| if @missing_values.key?(d) result << nil else acc += d result << acc end end Daru::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.
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# File 'lib/daru/maths/statistics/vector.rb', line 44 def describe methods=nil methods ||= [:count, :mean, :std, :min, :max] description = methods.map { |m| send(m) } Daru::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.
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# File 'lib/daru/maths/statistics/vector.rb', line 288 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) ⇒ Daru::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.
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# File 'lib/daru/maths/statistics/vector.rb', line 452 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) ⇒ Daru::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)
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# File 'lib/daru/maths/statistics/vector.rb', line 536 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 Daru::Vector.new(base, index: @index, name: @name) end |
#emsd(n = 10, wilder = false) ⇒ Daru::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.
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# File 'lib/daru/maths/statistics/vector.rb', line 607 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 Daru::Vector.new(result, index: @index, name: @name) end |
#emv(n = 10, wilder = false) ⇒ Daru::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.
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# File 'lib/daru/maths/statistics/vector.rb', line 570 def emv(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 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 Daru::Vector.new(var_base, index: @index, name: @name) end |
#factors ⇒ Object
Retrieve unique values of non-nil data
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# File 'lib/daru/maths/statistics/vector.rb', line 66 def factors only_valid.uniq.reset_index! end |
#freqs ⇒ Object
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# File 'lib/daru/maths/statistics/vector.rb', line 96 def freqs Daru::Vector.new(frequencies) end |
#frequencies ⇒ Object
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# File 'lib/daru/maths/statistics/vector.rb', line 90 def frequencies @data.each_with_object(Hash.new(0)) do |element, hash| hash[element] += 1 unless element.nil? end end |
#kurtosis(m = nil) ⇒ Object
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# File 'lib/daru/maths/statistics/vector.rb', line 228 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 - @missing_positions.size) * standard_deviation_sample(m) ** 4) - 3 end end |
#macd(fast = 12, slow = 26, signal = 9) ⇒ Object
Moving Average Convergence-Divergence. Calculates the MACD (moving average convergence-divergence) of the time series - this is a comparison of a fast EMA with a slow EMA.
Arguments
-
fast: integer, (default = 12) - fast component of MACD
-
slow: integer, (default = 26) - slow component of MACD
-
signal: integer, (default = 9) - signal component of MACD
Usage
ts = Daru::Vector.new((1..100).map { rand })
# => [0.69, 0.23, 0.44, 0.71, ...]
ts.macd(13)
Returns
Array of two Daru::Vectors - comparison of fast EMA with slow and EMA with signal value
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# File 'lib/daru/maths/statistics/vector.rb', line 635 def macd(fast=12, slow=26, signal=9) series = ema(fast) - ema(slow) [series, series.ema(signal)] end |
#max(return_type = :stored_type) ⇒ Object
Maximum element of the vector.
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# File 'lib/daru/maths/statistics/vector.rb', line 75 def max return_type=:stored_type max_value = @data.max if return_type == :vector Daru::Vector.new({index_of(max_value) => max_value}, name: @name, dtype: @dtype) else max_value end end |
#max_index ⇒ Daru::Vector
Return a Vector with the max element and its index.
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# File 'lib/daru/maths/statistics/vector.rb', line 86 def max_index max :vector end |
#mean ⇒ Object
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# File 'lib/daru/maths/statistics/vector.rb', line 8 def mean @data.mean end |
#median ⇒ Object
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# File 'lib/daru/maths/statistics/vector.rb', line 28 def median @data.respond_to?(:median) ? @data.median : percentile(50) end |
#median_absolute_deviation ⇒ Object Also known as: mad
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# File 'lib/daru/maths/statistics/vector.rb', line 50 def median_absolute_deviation m = median recode { |val| (val - m).abs }.median end |
#min ⇒ Object
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# File 'lib/daru/maths/statistics/vector.rb', line 20 def min @data.min end |
#mode ⇒ Object
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# File 'lib/daru/maths/statistics/vector.rb', line 32 def mode frequencies.max { |a,b| a[1]<=>b[1] }.first end |
#percent_change(periods = 1) ⇒ Object
The percent_change method computes the percent change over the given number of periods.
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# File 'lib/daru/maths/statistics/vector.rb', line 417 def percent_change periods=1 type == :numeric or raise TypeError, 'Vector must be numeric' value = only_valid arr = [] i = 1 ind = @data.find_index { |x| !x.nil? } (periods...size).each do |j| if j==ind || @missing_values.key?(@data[j]) arr[j] = nil else arr[j] = (value.data[i] - value.data[i - 1]) / value.data[i - 1].to_f i+=1 end end Daru::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)
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# File 'lib/daru/maths/statistics/vector.rb', line 256 def percentile(q, strategy=:midpoint) sorted = only_valid(:array).sort case strategy when :midpoint v = (n_valid * q).quo(100) if v.to_i!=v sorted[v.to_i] else (sorted[(v-0.5).to_i].to_f + sorted[(v+0.5).to_i]).quo(2) end when :linear index = (q / 100.0) * (n_valid + 1) k = index.truncate d = index % 1 if k == 0 sorted[0] elsif k >= sorted.size sorted[-1] else sorted[k - 1] + d * (sorted[k] - sorted[k - 1]) end else raise NotImplementedError, "Unknown strategy #{strategy}" end end |
#product ⇒ Object
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# File 'lib/daru/maths/statistics/vector.rb', line 16 def product @data.product end |
#proportion(value = 1) ⇒ Object
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# File 'lib/daru/maths/statistics/vector.rb', line 144 def proportion value=1 frequencies[value].quo(n_valid).to_f end |
#proportions ⇒ Object
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# File 'lib/daru/maths/statistics/vector.rb', line 100 def proportions len = n_valid frequencies.each_with_object({}) { |arr, hash| hash[arr[0]] = arr[1] / len } end |
#range ⇒ Object
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# File 'lib/daru/maths/statistics/vector.rb', line 24 def range max - min end |
#ranked ⇒ Object
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# File 'lib/daru/maths/statistics/vector.rb', line 105 def ranked sum = 0 r = frequencies.sort.each_with_object({}) do |val, memo| memo[val[0]] = ((sum + 1) + (sum + val[1])).quo(2) sum += val[1] end recode { |e| r[e] } end |
#rolling(function, n = 10) ⇒ Daru::Vector
Calculate the rolling function for a loopback value.
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# File 'lib/daru/maths/statistics/vector.rb', line 474 def rolling function, n=10 Daru::Vector.new( [nil] * (n - 1) + (0..(size - n)).map do |i| Daru::Vector.new(@data[i...(i + n)]).send(function) end, index: @index ) end |
#rolling_count ⇒ Object
Calculate rolling non-missing count
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# File 'lib/daru/maths/statistics/vector.rb', line 507 [:count, :mean, :median, :max, :min, :sum, :std, :variance].each do |meth| define_method("rolling_#{meth}".to_sym) do |n=10| rolling(meth, n) end end |
#rolling_max ⇒ Object
Calculate rolling max value
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# File 'lib/daru/maths/statistics/vector.rb', line 507 [:count, :mean, :median, :max, :min, :sum, :std, :variance].each do |meth| define_method("rolling_#{meth}".to_sym) do |n=10| rolling(meth, n) end end |
#rolling_mean ⇒ Object
Calculate rolling average
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# File 'lib/daru/maths/statistics/vector.rb', line 507 [:count, :mean, :median, :max, :min, :sum, :std, :variance].each do |meth| define_method("rolling_#{meth}".to_sym) do |n=10| rolling(meth, n) end end |
#rolling_median ⇒ Object
Calculate rolling median
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# File 'lib/daru/maths/statistics/vector.rb', line 507 [:count, :mean, :median, :max, :min, :sum, :std, :variance].each do |meth| define_method("rolling_#{meth}".to_sym) do |n=10| rolling(meth, n) end end |
#rolling_min ⇒ Object
Calculate rolling min value
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# File 'lib/daru/maths/statistics/vector.rb', line 507 [:count, :mean, :median, :max, :min, :sum, :std, :variance].each do |meth| define_method("rolling_#{meth}".to_sym) do |n=10| rolling(meth, n) end end |
#rolling_std ⇒ Object
Calculate rolling standard deviation
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# File 'lib/daru/maths/statistics/vector.rb', line 507 [:count, :mean, :median, :max, :min, :sum, :std, :variance].each do |meth| define_method("rolling_#{meth}".to_sym) do |n=10| rolling(meth, n) end end |
#rolling_sum ⇒ Object
Calculate rolling sum
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# File 'lib/daru/maths/statistics/vector.rb', line 507 [:count, :mean, :median, :max, :min, :sum, :std, :variance].each do |meth| define_method("rolling_#{meth}".to_sym) do |n=10| rolling(meth, n) end end |
#rolling_variance ⇒ Object
Calculate rolling variance
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# File 'lib/daru/maths/statistics/vector.rb', line 507 [:count, :mean, :median, :max, :min, :sum, :std, :variance].each do |meth| define_method("rolling_#{meth}".to_sym) 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.
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# File 'lib/daru/maths/statistics/vector.rb', line 366 def sample_with_replacement(sample=1) if @data.respond_to? :sample_with_replacement @data.sample_with_replacement sample else valid = missing_positions.empty? ? self : only_valid 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.
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# File 'lib/daru/maths/statistics/vector.rb', line 382 def sample_without_replacement(sample=1) if @data.respond_to? :sample_without_replacement @data.sample_without_replacement sample else valid = missing_positions.empty? ? self : only_valid raise ArgumentError, "Sample size couldn't be greater than n" if sample > valid.size out = [] size = valid.size while out.size < sample value = rand(size) out.push(value) unless out.include?(value) end out.collect { |i| valid[i] } end end |
#skew(m = nil) ⇒ Object
Calculate skewness using (sigma(xi - mean)^3)/((N)*std_dev_sample^3)
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# File 'lib/daru/maths/statistics/vector.rb', line 218 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 - @missing_positions.size) * (standard_deviation_sample(m)**3)) end end |
#standard_deviation_population(m = nil) ⇒ Object Also known as: sdp
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# File 'lib/daru/maths/statistics/vector.rb', line 199 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
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# File 'lib/daru/maths/statistics/vector.rb', line 208 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
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# File 'lib/daru/maths/statistics/vector.rb', line 57 def standard_error standard_deviation_sample/Math.sqrt(n_valid) 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.
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# File 'lib/daru/maths/statistics/vector.rb', line 313 def standardize use_population=false m ||= mean sd = use_population ? sdp : sds return Daru::Vector.new([nil]*@size) if m.nil? || sd == 0.0 vector_standardized_compute m, sd end |
#sum ⇒ Object
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# File 'lib/daru/maths/statistics/vector.rb', line 12 def sum @data.sum end |
#sum_of_squared_deviation ⇒ Object
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# File 'lib/daru/maths/statistics/vector.rb', line 61 def sum_of_squared_deviation (@data.inject(0) { |a,x| x.square + a } - sum.square.quo(n_valid).to_f).to_f end |
#sum_of_squares(m = nil) ⇒ Object Also known as: ss
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# File 'lib/daru/maths/statistics/vector.rb', line 192 def sum_of_squares(m=nil) m ||= mean @data.inject(0) { |memo, val| @missing_values.key?(val) ? memo : (memo + (val - m)**2) } end |
#value_counts ⇒ Object
Count number of occurrences of each value in the Vector
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# File 'lib/daru/maths/statistics/vector.rb', line 135 def value_counts values = {} @data.each do |d| values[d] ? values[d] += 1 : values[d] = 1 end Daru::Vector.new(values) end |
#variance_population(m = nil) ⇒ Object
Population variance with denominator (N)
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# File 'lib/daru/maths/statistics/vector.rb', line 159 def variance_population m=nil m ||= mean if @data.respond_to? :variance_population @data.variance_population m else sum_of_squares(m).quo(n_valid).to_f end end |
#variance_sample(m = nil) ⇒ Object Also known as: variance
Sample variance with denominator (N-1)
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# File 'lib/daru/maths/statistics/vector.rb', line 149 def variance_sample m=nil m ||= mean if @data.respond_to? :variance_sample @data.variance_sample m else sum_of_squares(m).quo(n_valid - 1) end end |
#vector_centered_compute(m) ⇒ Object
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# File 'lib/daru/maths/statistics/vector.rb', line 352 def vector_centered_compute(m) if @data.respond_to? :vector_centered_compute @data.vector_centered_compute(m) else Daru::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.
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# File 'lib/daru/maths/statistics/vector.rb', line 338 def vector_percentile c = size - missing_positions.size ranked.recode! { |i| i.nil? ? nil : (i.quo(c)*100).to_f } end |
#vector_standardized_compute(m, sd) ⇒ Object
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# File 'lib/daru/maths/statistics/vector.rb', line 343 def vector_standardized_compute(m,sd) if @data.respond_to? :vector_standardized_compute @data.vector_standardized_compute(m,sd) else Daru::Vector.new @data.collect { |x| x.nil? ? nil : (x.to_f - m).quo(sd) }, index: index, name: name, dtype: dtype end end |