001package org.hl7.fhir.dstu3.model.codesystems;
002
003
004
005
006/*
007  Copyright (c) 2011+, HL7, Inc.
008  All rights reserved.
009  
010  Redistribution and use in source and binary forms, with or without modification, 
011  are permitted provided that the following conditions are met:
012  
013   * Redistributions of source code must retain the above copyright notice, this 
014     list of conditions and the following disclaimer.
015   * Redistributions in binary form must reproduce the above copyright notice, 
016     this list of conditions and the following disclaimer in the documentation 
017     and/or other materials provided with the distribution.
018   * Neither the name of HL7 nor the names of its contributors may be used to 
019     endorse or promote products derived from this software without specific 
020     prior written permission.
021  
022  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND 
023  ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED 
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025  IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, 
026  INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT 
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028  PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, 
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031  POSSIBILITY OF SUCH DAMAGE.
032  
033*/
034
035// Generated on Sat, Mar 25, 2017 21:03-0400 for FHIR v3.0.0
036
037
038import org.hl7.fhir.exceptions.FHIRException;
039
040public enum V3ProbabilityDistributionType {
041
042        /**
043         * The beta-distribution is used for data that is bounded on both sides and may or may not be skewed (e.g., occurs when probabilities are estimated.)  Two parameters a and b  are available to adjust the curve.  The mean m and variance s2 relate as follows: m = a/ (a + b) and s2 = ab/((a + b)2 (a + b + 1)).
044         */
045        B, 
046        /**
047         * Used for data that describes extinction.  The exponential distribution is a special form of g-distribution where a = 1, hence, the relationship to mean m and variance s2 are m = b and s2 = b2.
048         */
049        E, 
050        /**
051         * Used to describe the quotient of two c2 random variables.  The F-distribution has two parameters n1 and n2, which are the numbers of degrees of freedom of the numerator and denominator variable respectively. The relationship to mean m  and variance s2 are: m = n2 / (n2 - 2) and s2 = (2 n2 (n2 + n1 - 2)) / (n1 (n2 - 2)2 (n2 - 4)).
052         */
053        F, 
054        /**
055         * The gamma-distribution used for data that is skewed and bounded to the right, i.e. where the maximum of the distribution curve is located near the origin.  The g-distribution has a two parameters a and b.  The relationship to mean m and variance s2 is m = a b and s2 = a b2.
056         */
057        G, 
058        /**
059         * The logarithmic normal distribution is used to transform skewed random variable X into a normally distributed random variable U = log X. The log-normal distribution can be specified with the properties mean m and standard deviation s.  Note however that mean m and standard deviation s are the parameters of the raw value distribution, not the transformed parameters of the lognormal distribution that are conventionally referred to by the same letters.  Those log-normal parameters mlog and slog relate to the mean m and standard deviation s of the data value through slog2 = log (s2/m2 + 1) and mlog = log m - slog2/2.
060         */
061        LN, 
062        /**
063         * This is the well-known bell-shaped normal distribution.  Because of the central limit theorem, the normal distribution is the distribution of choice for an unbounded random variable that is an outcome of a combination of many stochastic processes.  Even for values bounded on a single side (i.e. greater than 0) the normal distribution may be accurate enough if the mean is "far away" from the bound of the scale measured in terms of standard deviations.
064         */
065        N, 
066        /**
067         * Used to describe the quotient of a normal random variable and the square root of a c2 random variable.  The t-distribution has one parameter n, the number of degrees of freedom. The relationship to mean m  and variance s2 are: m = 0 and s2 = n / (n - 2)
068         */
069        T, 
070        /**
071         * The uniform distribution assigns a constant probability over the entire interval of possible outcomes, while all outcomes outside this interval are assumed to have zero probability.  The width of this interval is 2s sqrt(3).  Thus, the uniform distribution assigns the probability densities f(x) = sqrt(2 s sqrt(3))  to values m - s sqrt(3) >= x <= m + s sqrt(3) and f(x) = 0 otherwise.
072         */
073        U, 
074        /**
075         * Used to describe the sum of squares of random variables which occurs when a variance is estimated (rather than presumed) from the sample.  The only parameter of the c2-distribution is n, so called the number of degrees of freedom (which is the number of independent parts in the sum).  The c2-distribution is a special type of g-distribution with parameter a = n /2 and b  = 2.  Hence, m = n and s2 = 2 n.
076         */
077        X2, 
078        /**
079         * added to help the parsers
080         */
081        NULL;
082        public static V3ProbabilityDistributionType fromCode(String codeString) throws FHIRException {
083            if (codeString == null || "".equals(codeString))
084                return null;
085        if ("B".equals(codeString))
086          return B;
087        if ("E".equals(codeString))
088          return E;
089        if ("F".equals(codeString))
090          return F;
091        if ("G".equals(codeString))
092          return G;
093        if ("LN".equals(codeString))
094          return LN;
095        if ("N".equals(codeString))
096          return N;
097        if ("T".equals(codeString))
098          return T;
099        if ("U".equals(codeString))
100          return U;
101        if ("X2".equals(codeString))
102          return X2;
103        throw new FHIRException("Unknown V3ProbabilityDistributionType code '"+codeString+"'");
104        }
105        public String toCode() {
106          switch (this) {
107            case B: return "B";
108            case E: return "E";
109            case F: return "F";
110            case G: return "G";
111            case LN: return "LN";
112            case N: return "N";
113            case T: return "T";
114            case U: return "U";
115            case X2: return "X2";
116            case NULL: return null;
117            default: return "?";
118          }
119        }
120        public String getSystem() {
121          return "http://hl7.org/fhir/v3/ProbabilityDistributionType";
122        }
123        public String getDefinition() {
124          switch (this) {
125            case B: return "The beta-distribution is used for data that is bounded on both sides and may or may not be skewed (e.g., occurs when probabilities are estimated.)  Two parameters a and b  are available to adjust the curve.  The mean m and variance s2 relate as follows: m = a/ (a + b) and s2 = ab/((a + b)2 (a + b + 1)).";
126            case E: return "Used for data that describes extinction.  The exponential distribution is a special form of g-distribution where a = 1, hence, the relationship to mean m and variance s2 are m = b and s2 = b2.";
127            case F: return "Used to describe the quotient of two c2 random variables.  The F-distribution has two parameters n1 and n2, which are the numbers of degrees of freedom of the numerator and denominator variable respectively. The relationship to mean m  and variance s2 are: m = n2 / (n2 - 2) and s2 = (2 n2 (n2 + n1 - 2)) / (n1 (n2 - 2)2 (n2 - 4)).";
128            case G: return "The gamma-distribution used for data that is skewed and bounded to the right, i.e. where the maximum of the distribution curve is located near the origin.  The g-distribution has a two parameters a and b.  The relationship to mean m and variance s2 is m = a b and s2 = a b2.";
129            case LN: return "The logarithmic normal distribution is used to transform skewed random variable X into a normally distributed random variable U = log X. The log-normal distribution can be specified with the properties mean m and standard deviation s.  Note however that mean m and standard deviation s are the parameters of the raw value distribution, not the transformed parameters of the lognormal distribution that are conventionally referred to by the same letters.  Those log-normal parameters mlog and slog relate to the mean m and standard deviation s of the data value through slog2 = log (s2/m2 + 1) and mlog = log m - slog2/2.";
130            case N: return "This is the well-known bell-shaped normal distribution.  Because of the central limit theorem, the normal distribution is the distribution of choice for an unbounded random variable that is an outcome of a combination of many stochastic processes.  Even for values bounded on a single side (i.e. greater than 0) the normal distribution may be accurate enough if the mean is \"far away\" from the bound of the scale measured in terms of standard deviations.";
131            case T: return "Used to describe the quotient of a normal random variable and the square root of a c2 random variable.  The t-distribution has one parameter n, the number of degrees of freedom. The relationship to mean m  and variance s2 are: m = 0 and s2 = n / (n - 2)";
132            case U: return "The uniform distribution assigns a constant probability over the entire interval of possible outcomes, while all outcomes outside this interval are assumed to have zero probability.  The width of this interval is 2s sqrt(3).  Thus, the uniform distribution assigns the probability densities f(x) = sqrt(2 s sqrt(3))  to values m - s sqrt(3) >= x <= m + s sqrt(3) and f(x) = 0 otherwise.";
133            case X2: return "Used to describe the sum of squares of random variables which occurs when a variance is estimated (rather than presumed) from the sample.  The only parameter of the c2-distribution is n, so called the number of degrees of freedom (which is the number of independent parts in the sum).  The c2-distribution is a special type of g-distribution with parameter a = n /2 and b  = 2.  Hence, m = n and s2 = 2 n.";
134            case NULL: return null;
135            default: return "?";
136          }
137        }
138        public String getDisplay() {
139          switch (this) {
140            case B: return "beta";
141            case E: return "exponential";
142            case F: return "F";
143            case G: return "(gamma)";
144            case LN: return "log-normal";
145            case N: return "normal (Gaussian)";
146            case T: return "T";
147            case U: return "uniform";
148            case X2: return "chi square";
149            case NULL: return null;
150            default: return "?";
151          }
152    }
153
154
155}