#!/usr/bin/env python
# coding: utf-8

# In[1]:


import numpy as np


# I will implement the 10-year risk equation for women. Here are the betas.

# In[2]:


betas = np.array([2.72107, # log age
0.51125,          # log BMI
2.81291,          # log SBP (not treated)
2.88267,          # log SBP (treated)
0.61868,          # smoking
0.77763,          # diabetes
])


# Here is the formula. The `dot` function is the dot product, or inner product of the coefficients and the data array (i.e. multiply them element-wise, and add the products)

# In[16]:


def frs(X, b, surv, const):
    return 1 - surv** np.exp(X.dot(b) - const)


# Below are special cases of the general formula, using the survival and constants specified for men and women:

# In[17]:


def frs_women(X, b):
    return frs(X, b, 0.94833, 26.0145)


# In[18]:


def frs_men(X, b):
    return frs(X, b, 0.88431, 23.9388)


# Here is some sample data

# In[19]:


x_sample = np.array([np.log(30), np.log(22.5), 0, np.log(125), 0, 0])
x_sample


# Here is the corresponding risk scores (men and women)

# In[20]:


frs_women(X=x_sample, b=betas)


# In[21]:


frs_men(X=x_sample, b=betas)