[apps/probability] Improve law models

Change-Id: Ibfbf9df3f7c94b0fb1c4cef6900b5680e5ea3b7d

apps/probability/law/binomial_law.cpp

@@ -5,7 +5,7 @@
 namespace Probability {
 
 BinomialLaw::BinomialLaw() :
-  TwoParameterLaw()
+  TwoParameterLaw(20.0f, 0.5f)
 {
 }
 
@@ -44,23 +44,41 @@ float BinomialLaw::xMin() {
 }
 
 float BinomialLaw::xMax() {
-  if (m_parameter1 == 0) {
-    return 1.0f;
-  }
-  return m_parameter1;
+  return m_parameter1 + 1.0f;
 }
 
 float BinomialLaw::yMin() {
-  return -0.2f;
+  int maxAbscissa = m_parameter2 < 1.0f ? (m_parameter1+1)*m_parameter2 : m_parameter1;
+  float result = k_minMarginFactor*evaluateAtAbscissa(maxAbscissa);
+  if (result >= 0.0f || isnan(result)) {
+    result = -0.2f;
+  }
+  return result;
 }
 
 float BinomialLaw::yMax() {
-  return 1.0f;
+  int maxAbscissa = m_parameter2 < 1.0f ? (m_parameter1+1)*m_parameter2 : m_parameter1;
+  float result = k_maxMarginFactor*evaluateAtAbscissa(maxAbscissa);
+  if (result <= 0.0f || result == yMin() || isnan(result)) {
+    result = yMin() + 1.0f;
+  }
+  return result;
 }
 
 float BinomialLaw::evaluateAtAbscissa(float x) const {
-  //m_expression = Expression::parse("binomial(a, b)*b^t*(1-b)^(a-t)");
-  return powf(m_parameter2, (int)x)*powf(1-m_parameter2, m_parameter1-(int)x);
+  if (m_parameter1 == 0.0f && (m_parameter2 == 0.0f || m_parameter2 == 1.0f)) {
+    return NAN;
+  }
+  if (m_parameter2 == 1.0f) {
+    if ((int)x == m_parameter1) {
+      return 1.0f;
+    }
+    return 0.0f;
+  }
+  if (x > m_parameter1) {
+    return 0.0f;
+  }
+  return expf(lgammaf(m_parameter1+1) - lgammaf((int)x+1) - lgammaf(m_parameter1 - (int)x+1))*powf(m_parameter2, (int)x)*powf(1-m_parameter2, m_parameter1-(int)x);
 }
 
 }

apps/probability/law/exponential_law.cpp

@@ -5,7 +5,7 @@
 namespace Probability {
 
 ExponentialLaw::ExponentialLaw() :
-  OneParameterLaw()
+  OneParameterLaw(1.0f)
 {
 }
 
@@ -36,23 +36,20 @@ float ExponentialLaw::xMin() {
 }
 
 float ExponentialLaw::xMax() {
-  if (m_parameter1 == 0.0f) {
-    return 100.0f;
-  }
+  assert(m_parameter1 != 0.0f);
   return 5.0f/m_parameter1;
 }
 
 float ExponentialLaw::yMin() {
-  return -0.2f;
+  return k_minMarginFactor*m_parameter1;
 }
 
 float ExponentialLaw::yMax() {
-  return 1.0f;
+  return k_maxMarginFactor*m_parameter1;
 }
 
 float ExponentialLaw::evaluateAtAbscissa(float x) const {
-// TODO: change 2.7f for the right constant
-  return m_parameter1*powf(2.7f, -m_parameter1*x);
+  return m_parameter1*expf(-m_parameter1*x);
 }
 
 }

apps/probability/law/law.h

@@ -35,6 +35,8 @@ protected:
   constexpr static float k_oneUnit = 1.0f;
   constexpr static float k_twoUnit = 2.0f;
   constexpr static float k_fiveUnit = 5.0f;
+  constexpr static float k_minMarginFactor = -0.2f;
+  constexpr static float k_maxMarginFactor = 1.2f;
 };
 
 }

apps/probability/law/normal_law.cpp

@@ -5,7 +5,7 @@
 namespace Probability {
 
 NormalLaw::NormalLaw() :
-  TwoParameterLaw()
+  TwoParameterLaw(0.0f, 1.0f)
 {
 }
 
@@ -54,16 +54,28 @@ float NormalLaw::xMax() {
 }
 
 float NormalLaw::yMin() {
-  return -0.2f;
+  float maxAbscissa = m_parameter1;
+  float result = k_minMarginFactor*evaluateAtAbscissa(maxAbscissa);
+  if (result >= 0.0f) {
+    result = -0.2f;
+  }
+  return result;
 }
 
 float NormalLaw::yMax() {
-  return 1.0f;
+  float maxAbscissa = m_parameter1;
+  float result = k_maxMarginFactor*evaluateAtAbscissa(maxAbscissa);
+  if (result <= 0.0f || result == yMin()) {
+    result = yMin() + 1.0f;
+  }
+  return result;
 }
 
 float NormalLaw::evaluateAtAbscissa(float x) const {
-  // TODO: 3.14f -> Pi and 2.7f -> e
-  return (1.0f/(m_parameter2*sqrtf(2*3.14f)))*powf(2.7f, -0.5f*powf((x-m_parameter1)/m_parameter2,2));
+  if (m_parameter2 == 0.0f) {
+    return NAN;
+  }
+  return (1.0f/(m_parameter2*sqrtf(2.0f*M_PI)))*expf(-0.5f*powf((x-m_parameter1)/m_parameter2,2));
 }
 
 }

apps/probability/law/one_parameter_law.cpp

@@ -3,8 +3,8 @@
 
 namespace Probability {
 
-OneParameterLaw::OneParameterLaw() :
-  m_parameter1(0.5f)
+OneParameterLaw::OneParameterLaw(float parameterValue) :
+  m_parameter1(parameterValue)
 {
 }
 

apps/probability/law/one_parameter_law.h

@@ -7,7 +7,7 @@ namespace Probability {
 
 class OneParameterLaw : public Law {
 public:
-  OneParameterLaw();
+  OneParameterLaw(float parameterValue);
   virtual ~OneParameterLaw() {};
   int numberOfParameter() override;
   float parameterValueAtIndex(int index) override;

apps/probability/law/poisson_law.cpp

@@ -5,7 +5,7 @@
 namespace Probability {
 
 PoissonLaw::PoissonLaw() :
-  OneParameterLaw()
+  OneParameterLaw(4.0f)
 {
 }
 
@@ -36,23 +36,22 @@ float PoissonLaw::xMin() {
 }
 
 float PoissonLaw::xMax() {
-  if (m_parameter1 == 0.0f) {
-    return 1.0f;
-  }
+  assert(m_parameter1 != 0);
   return m_parameter1 + 5.0f*sqrtf(m_parameter1);
 }
 
 float PoissonLaw::yMin() {
-  return -0.2f;
+  int maxAbscissa = (int)m_parameter1;
+  return k_minMarginFactor*evaluateAtAbscissa(maxAbscissa);
 }
 
 float PoissonLaw::yMax() {
-  return 1.0f;
+  int maxAbscissa = (int)m_parameter1;
+  return k_maxMarginFactor*evaluateAtAbscissa(maxAbscissa);
 }
 
 float PoissonLaw::evaluateAtAbscissa(float x) const {
-  // TODO: 2.7f -> e and factio
-  return powf(2.7f, -m_parameter1)*powf(m_parameter1, x)/(x);
+  return expf(-m_parameter1)*powf(m_parameter1, (int)x)/expf(lgammaf((int)x+1));
 }
 
 }

apps/probability/law/two_parameter_law.cpp

@@ -3,9 +3,9 @@
 
 namespace Probability {
 
-TwoParameterLaw::TwoParameterLaw() :
-  m_parameter1(0.0f),
-  m_parameter2(0.0f)
+TwoParameterLaw::TwoParameterLaw(float parameterValue1, float parameterValue2) :
+  m_parameter1(parameterValue1),
+  m_parameter2(parameterValue2)
 {
 }
 

apps/probability/law/two_parameter_law.h

@@ -7,7 +7,7 @@ namespace Probability {
 
 class TwoParameterLaw : public Law {
 public:
-  TwoParameterLaw();
+  TwoParameterLaw(float parameterValue1, float parameterValue2);
   virtual ~TwoParameterLaw() {};
   int numberOfParameter() override;
   float parameterValueAtIndex(int index) override;

apps/probability/law/uniform_law.cpp

@@ -4,7 +4,7 @@
 namespace Probability {
 
 UniformLaw::UniformLaw() :
-  TwoParameterLaw()
+  TwoParameterLaw(-1.0f, 1.0f)
 {
 }
 
@@ -39,25 +39,20 @@ const char * UniformLaw::parameterDefinitionAtIndex(int index) {
 }
 
 float UniformLaw::xMin() {
-  if (m_parameter2 < m_parameter1) {
-    return m_parameter2 - 3.0f;
-  }
-  return m_parameter1 - 3.0f;
+  assert(m_parameter2 >= m_parameter1);
+  return m_parameter1 - 0.6f*(m_parameter2 - m_parameter1);
 }
 
 float UniformLaw::xMax() {
-  if (m_parameter2 < m_parameter1) {
-    return m_parameter1 + 3.0f;
-  }
-  return m_parameter2 + 3.0f;;
+  return m_parameter2 + 0.6f*(m_parameter2 - m_parameter1);
 }
 
 float UniformLaw::yMin() {
-  return -0.2f;
+  return k_minMarginFactor*(1.0f/(m_parameter2-m_parameter1));
 }
 
 float UniformLaw::yMax() {
-  return 1.0f;
+  return k_maxMarginFactor*(1.0f/(m_parameter2-m_parameter1));
 }
 
 float UniformLaw::evaluateAtAbscissa(float t) const {