matlib.cpp
#include "matlib.h"
#include "geometry.h"
#include "LineChart.h"
#include "Histogram.h"
#include "CallOption.h"
using namespace std;
/* Create a linearly spaced vector */
vector<double> linspace( double from, double to, int numPoints ) {
ASSERT( numPoints>=2 );
vector<double> ret(numPoints,0.0);
double step = (to-from)/(numPoints-1);
double current = from;
for (int i=0; i<numPoints; i++) {
ret[i]=current;
current+=step;
}
return ret;
}
/**
* Find the sum of the elements in an array
*/
double sum( const std::vector<double>& v ) {
double total = 0.0;
int n = v.size();
for (int i=0; i<n; i++) {
total+= v[i];
}
return total;
}
/* Compute the mean of a vector */
double mean( const vector<double>& v ) {
int n = v.size();
ASSERT( n > 0);
return sum(v)/n;
}
/* Compute the standard deviation of a vector */
double standardDeviation( const vector<double>& v, bool population ) {
int n = v.size();
double total = 0.0;
double totalSq = 0.0;
for (int i=0; i<n; i++) {
total+=v[i];
totalSq+=v[i]*v[i];
}
if (population) {
ASSERT( n > 0 );
return sqrt( (totalSq - total*total/n)/n );
} else {
ASSERT( n > 1 );
return sqrt( (totalSq - total*total/n)/(n-1) );
}
}
/* Find the minimum of a vector */
double min( const vector<double>& v ) {
int n = v.size();
ASSERT( n > 0);
double min = v[0];
for (int i=1; i<n; i++) {
if (v[i]<min) {
min=v[i];
}
}
return min;
}
/* Find the maximum of a vector */
double max( const vector<double>& v ) {
int n = v.size();
ASSERT( n > 0);
double max = v[0];
for (int i=1; i<n; i++) {
if (v[i]>max) {
max=v[i];
}
}
return max;
}
/* Create uniformly distributed random numbers using the C random number API*/
vector<double> randuniformOld( int n ) {
vector<double> ret(n, 0.0);
for (int i=0; i<n; i++) {
int randInt = rand();
ret[i] = (randInt + 0.5)/(RAND_MAX+1.0);
}
return ret;
}
/* MersenneTwister random number generator */
static mt19937 mersenneTwister;
/* Reset the random number generator. We've borrowed the library call
from MATLAB, though we're ignoring the description string */
void rng( const string& description ) {
ASSERT( description=="default" );
mersenneTwister.seed(mt19937::default_seed);
}
/* Create uniformly distributed random numbers using
the Mersenne Twister algorithm. See the code above for the answer
to the homework excercise which should familiarize you with the C API*/
vector<double> randuniform( int n ) {
vector<double> ret(n, 0.0);
for (int i=0; i<n; i++) {
ret[i] = (mersenneTwister()+0.5)/(mersenneTwister.max()+1.0);
}
return ret;
}
/* Create normally distributed random numbers */
vector<double> randn( int n ) {
vector<double> v=randuniform(n);
for (int i=0; i<n; i++) {
v[i] = norminv(v[i]);
}
return v;
}
/**
* Sort a vector of doubles
*/
std::vector<double> sort( const std::vector<double>& v ) {
std::vector<double> copy(v);
std::sort( copy.begin(), copy.end() );
return copy;
}
/**
* Find the given percentile of a distribution
*/
double prctile( const std::vector<double>& v, double percentage ) {
// See the text for a precise specification
//
ASSERT( percentage >=0.0 );
ASSERT( percentage <=100.0 );
int n = v.size();
vector<double> sorted = sort( v );
int indexBelow = (int)(n* percentage/100.0 - 0.5);
int indexAbove = indexBelow + 1;
if (indexAbove > n-1 ) {
return sorted[n-1];
} if (indexBelow<0) {
return sorted[0];
}
double valueBelow = sorted[ indexBelow ];
double valueAbove = sorted[ indexAbove ];
double percentageBelow = 100.0*(indexBelow+0.5)/n;
double percentageAbove = 100.0*(indexAbove+0.5)/n;
if (percentage<=percentageBelow) {
return valueBelow;
}
if (percentage>=percentageAbove) {
return valueAbove;
}
double correction = (percentage - percentageBelow)*(valueAbove-valueBelow)/(percentageAbove-percentageBelow);
return valueBelow + correction;
}
/**
* Convenience method for generating plots
*/
void plot( const string& file,
const vector<double>& x,
const vector<double>& y ) {
LineChart lc;
lc.setSeries(x,y);
lc.writeAsHTML( file );
}
/**
* Convenience method for generating plots
*/
void hist( const string& file,
const vector<double>& data,
int numBuckets ) {
Histogram h;
h.setData(data);
h.setNumBuckets( numBuckets );
h.writeAsHTML( file );
}
const double ROOT_2_PI = sqrt( 2.0 * PI );
static inline double hornerFunction( double x, double a0, double a1) {
return a0 + x*a1;
}
static inline double hornerFunction( double x, double a0, double a1, double a2) {
return a0 + x*hornerFunction( x, a1, a2);
}
static inline double hornerFunction( double x, double a0, double a1, double a2, double a3) {
return a0 + x*hornerFunction( x, a1, a2, a3);
}
static inline double hornerFunction( double x, double a0, double a1, double a2, double a3, double a4) {
return a0 + x*hornerFunction( x, a1, a2, a3, a4);
}
static inline double hornerFunction( double x, double a0, double a1, double a2, double a3, double a4,
double a5) {
return a0 + x*hornerFunction( x, a1, a2, a3, a4, a5);
}
static inline double hornerFunction( double x, double a0, double a1, double a2, double a3, double a4,
double a5, double a6) {
return a0 + x*hornerFunction( x, a1, a2, a3, a4, a5, a6);
}
static inline double hornerFunction( double x, double a0, double a1, double a2, double a3, double a4,
double a5, double a6, double a7) {
return a0 + x*hornerFunction( x, a1, a2, a3, a4, a5, a6, a7);
}
static inline double hornerFunction( double x, double a0, double a1, double a2, double a3, double a4,
double a5, double a6, double a7, double a8) {
return a0 + x*hornerFunction( x, a1, a2, a3, a4, a5, a6, a7, a8);
}
/**
* Arguably this is a little easier to read than the original normcdf
* function as it makes the use of horner's method obvious.
*/
double normcdf( double x ) {
DEBUG_PRINT( "normcdf("<<x<<")");
if (x<=0) {
return 1-normcdf(-x);
}
double k = 1/(1 + 0.2316419*x);
double poly = hornerFunction(k,
0.0, 0.319381530, -0.356563782,
1.781477937,-1.821255978,1.330274429);
double approx = 1.0 - 1.0/ROOT_2_PI * exp(-0.5*x*x) * poly;
return approx;
}
/* Constants required for Moro's algorithm */
static const double a0 = 2.50662823884;
static const double a1 = -18.61500062529;
static const double a2 = 41.39119773534;
static const double a3 = -25.44106049637;
static const double b1 = -8.47351093090;
static const double b2 = 23.08336743743;
static const double b3 = -21.06224101826;
static const double b4 = 3.13082909833;
static const double c0 = 0.3374754822726147;
static const double c1 = 0.9761690190917186;
static const double c2 = 0.1607979714918209;
static const double c3 = 0.0276438810333863;
static const double c4 = 0.0038405729373609;
static const double c5 = 0.0003951896511919;
static const double c6 = 0.0000321767881768;
static const double c7 = 0.0000002888167364;
static const double c8 = 0.0000003960315187;
double norminv( double x ) {
// We use Moro's algorithm
DEBUG_PRINT( "norminv(" << x <<")" );
double y = x - 0.5;
if (y<0.42 && y>-0.42) {
double r = y*y;
DEBUG_PRINT( "Case 1, r=" << r );
return y*hornerFunction(r,a0,a1,a2,a3)/hornerFunction(r,1.0,b1,b2,b3,b4);
} else {
double r;
if (y<0.0) {
r = x;
} else {
r = 1.0 - x;
}
DEBUG_PRINT( "Case 2, r=" << r);
double s = log( -log( r ));
double t = hornerFunction(s,c0,c1,c2,c3,c4,c5,c6,c7,c8);
if (x>0.5) {
return t;
} else {
return -t;
}
}
}
/**
* Evaluate an integral using the rectangle rule
*/
double integral( RealFunction& f,
double a,
double b,
int nPoints ) {
double h = (b-a)/nPoints;
double x = a + 0.5*h;
double total = 0.0;
for (int i=0; i<nPoints; i++) {
double y = f.evaluate(x);
total+=y;
x+=h;
}
return h*total;
}
///////////////////////////////////////////////
//
// TESTS
//
///////////////////////////////////////////////
static vector<double> createTestVector() {
vector<double> v;
v.push_back(1);
v.push_back(5);
v.push_back(3);
v.push_back(9);
v.push_back(7);
return v;
}
static void testLinspace() {
vector<double> result = linspace(1.0, 10.0, 4 );
ASSERT_APPROX_EQUAL( result[0], 1.0, 0.001 );
ASSERT_APPROX_EQUAL( result[1], 4.0, 0.001 );
ASSERT_APPROX_EQUAL( result[2], 7.0, 0.001 );
ASSERT_APPROX_EQUAL( result[3], 10.0, 0.001 );
}
static void testMean() {
ASSERT_APPROX_EQUAL( mean( createTestVector() ), 5.0, 0.001);
}
static void testStandardDeviation() {
ASSERT_APPROX_EQUAL( standardDeviation( createTestVector() ), 3.1623, 0.001);
ASSERT_APPROX_EQUAL( standardDeviation( createTestVector(), true ), 2.8284, 0.001);
}
static void testMin() {
ASSERT_APPROX_EQUAL( min( createTestVector() ), 1.0, 0.001);
}
static void testMax() {
ASSERT_APPROX_EQUAL( max( createTestVector() ), 9.0, 0.001);
}
static void testRanduniform() {
rng("default");
vector<double> v = randuniform(1000);
ASSERT( ((int)v.size())==1000 );
ASSERT_APPROX_EQUAL( mean(v), 0.5, 0.1);
ASSERT( max(v)<1.0);
ASSERT( min(v)>0.0);
}
static void testRandn() {
rng("default");
vector<double> v = randn(10000);
ASSERT( ((int)v.size())==10000 );
ASSERT_APPROX_EQUAL( mean(v), 0.0, 0.1);
ASSERT_APPROX_EQUAL( standardDeviation(v), 1.0, 0.1);
}
static void testNormCdf() {
ASSERT_APPROX_EQUAL( normcdf( 1.96 ), 0.975, 0.001 );
}
static void testNormInv() {
ASSERT_APPROX_EQUAL( norminv( 0.975 ), 1.96, 0.01 );
}
static void testPrctile() {
const vector<double> v = createTestVector();
ASSERT_APPROX_EQUAL( prctile( v, 100.0 ), 9.0, 0.001 );
ASSERT_APPROX_EQUAL( prctile( v, 0.0 ), 1.0, 0.001 );
ASSERT_APPROX_EQUAL( prctile( v, 50.0 ), 5.0, 0.001 );
ASSERT_APPROX_EQUAL( prctile( v, 17.0 ), 1.7, 0.001 );
ASSERT_APPROX_EQUAL( prctile( v, 62.0 ), 6.2, 0.001 );
}
/* To test the integral function, we need a function
to integrate */
class SinFunction : public RealFunction {
double evaluate( double x );
};
double SinFunction::evaluate( double x ) {
return sin(x);
}
static void testIntegral() {
SinFunction integrand;
double actual = integral(integrand, 1, 3, 1000 );
double expected = -cos(3.0)+cos(1.0);
ASSERT_APPROX_EQUAL( actual, expected, 0.000001);
}
/**
* When you create a small class like this, using
* nested classes is easier.
*/
static void testIntegralVersion2() {
class Sin : public RealFunction {
public:
double evaluate( double x ) {
return sin(x);
}
};
Sin integrand;
double actual = integral(integrand, 1, 3, 1000 );
double expected = -cos(3.0)+cos(1.0);
ASSERT_APPROX_EQUAL(actual, expected, 0.000001);
}
static double integratePayoff(
double a,
double b,
const PathIndependentOption& option) {
class PayoffFunction : public RealFunction {
public:
/* Member variable */
const PathIndependentOption& option;
/* Constructor */
PayoffFunction(
const PathIndependentOption& option)
: option( option ) {
}
/**
* Overriding function
*/
double evaluate(double x) {
return option.payoff(x);
}
};
PayoffFunction integrand(option);
return integral(integrand, a, b, 1000);
}
static void testIntegratePayoff() {
CallOption c;
c.strike = 0;
ASSERT_APPROX_EQUAL(integratePayoff(0,1,c), 0.5, 0.00001);
}
void testMatlib() {
TEST( testLinspace );
TEST( testMean );
TEST( testStandardDeviation );
TEST( testMin );
TEST( testMax );
TEST( testRanduniform );
TEST( testRandn );
TEST( testNormInv );
TEST( testNormCdf );
TEST( testPrctile );
TEST( testIntegral );
TEST( testIntegralVersion2 );
TEST(testIntegratePayoff);
}