# 3. Linear Classification¶

### Luis Martí¶

#### Instituto de Computação¶

$\renewcommand{\vec}[1]{\boldsymbol{#1}}$

# Classification¶

• Given an input vector $\vec{x}$ determine $p\left(\left. C_k\right|\vec{x}\right)$ where $k\in \{1\ldots K\}$ and $C_k$ is a discrete class label.
• We will discuss linear models for classification.

Note: the algorithms described here can also be applied to transformed input, $\phi(\vec{x})$, to determine $p\left(C_k\right|\left.\phi(\vec{x})\right)$, where $\phi(\cdot)$ may be a nonlinear transformation of the input space.

Our first model assumption is that our target output can be modeled as

$$y(\phi(\vec{x})) = f(\vec{w}^\intercal\phi(\vec{x}))$$

where $y$ will be a vector of probabilities with $K$ elements.

• Elements of $\vec{y}$ are $y_k = p\left(C_k\right|\left.\phi\left(\vec{x}\right)\right)$ i.e. the probability that the correct class label is $C_k$ given the input vector $\vec{x}$.
• Often we will have simply that $\vec{w}^\intercal\phi(\vec{x}) = \vec{w}^\intercal\vec{x}+w_0$ in which case we will simply omit $\phi()$ from the notation.
• The function $f()$ is known as the activation function and its inverse is known as the link function.
• Note that $f()$ will often be nonlinear.

The target variable, $y$, does not provide a decision for a class assignment for a given input $\vec{x}$.

• In real world cases where it is necessary to make a decision as to which class $\vec{x}$ should be assigned;
• one must apply an additional modeling step based on decision theory.

There are a variety of decision models, all of which leverage the class posterior probability models, $p\left(C_k\right|\left.\vec{x}\right)$, such as

• Minimizing the misclassification rate - this effectively corresponds to choosing the class with the highest probability for a given $\vec{x}$.
• Minimizing the expected loss - minimizes the expected value of a given loss function, $\ell(\cdot)$, under the class posterior probability distribution.
• Reject option.

## Linear classification models¶

The models discussed today are called linear because,

• when the decision criteria is that of minimizing the misclassification rate,
• they divide the input space into $K$ regions,
• where the boundaries between regions are linear functions of the input vector $\vec{x}$.
• The decision boundaries will correspond to where $\vec{w}^\intercal\vec{x}=\text{constant}$, and thus represent a linear function of $\vec{x}$.
• In the case of transformed input, $\phi(\vec{x})$, the decision boundaries will correspond to where $\vec{w}^\intercal\phi(\vec{x})=\text{constant}$, and thus represent a linear function of $\phi(\vec{x})$.

# Probabilistic generative models¶

Our first step is to form a model of the class posterior probabilities for the inference stage:

This can be done by modeling:

• the class-conditional densities, $p\left(\vec{x}\right|\left.C_k\right)$,
• the class priors, $p(C_k)$, and
• applying Bayes' theorem to obtain the posterior model, or

Note: It can also be done directly, as in probabilistic discriminative modeling (forward pointer).

## Remembering Bayes' theorem¶

Bayes' theorem describes the probability of an event, based on conditions that might be related to the event.

$$P\left(A\right|\left.B\right) = \frac{P\left(B\right|\left.A\right) \, P(A)}{P(B)}\,,$$

where $A$ and $B$ are events.

• $P(A)$ and $P(B)$ are the probabilities of $A$ and $B$ without regard to each other.
• $P\left( A \right|\left.B\right)$, a conditional probability, is the probability of observing event $A$ given that $B$ is true.
• $P\left(B\right|\left.A\right)$ is the probability of observing event $B$ given that $A$ is true.
• We will see that our class posterior probability model will depend only on the input $\vec{x}$...
• ...or a fixed transformation using basis functions $\phi()$, and
• class labels, $C_k$.

In this case, for $K$ classes, our activation function will take the form of the softmax function,

$$f_k = p\left(C_k\right|\left.\vec{x}\right) = \frac{p\left(\vec{x}\right|\left.C_k\right)p\left(C_k\right)}{p(\vec{x})} = \frac{\exp(a_k)}{\sum_j \exp(a_j)}\,,$$

where

$$a_j = \ln\left(p\left(\vec{x}\right|\left.C_k\right)p(C_k)\right).$$

In the case of $K=2$, the activation function will reduce to the logistic sigmoid function

$$f(\vec{x}) = p\left(C_1\right|\left.\vec{x}\right) = \frac{1}{1+\exp(-a)} = \sigma(a)\,,$$

where

$$a = \ln \frac {p\left(\vec{x}\right|\left. C_1\right)p(C_1)} {p\left(\vec{x}\right|\left. C_2\right)p\left(C_2\right)}\,.$$

These models are known as generative models because they can be used to generate synthetic input data by applying inverse transform sampling to the marginal distribution for $\vec{x}$:

$$p(\vec{x}) = \sum_k p(\vec{x}|C_k)p(C_k)\,.$$

### Model assumptions¶

To move forward, it is necessary to start making model assumptions. Here we will assume that:

• we have continuous inputs, i.e. $x\in \mathbb{R}^n$ (see Bishop p.202 or Andrew Ng's Lecture 5 pdf for discrete input using Naïve Bayes & Laplace), and
• that the class-conditional densities, $p(\vec{x}|C_k)$, are modeled by a Gaussian distribution.

### Transformation under the Gaussian assumption¶

Under the Gaussian assumption, the class-conditional density for class $C_k$ is

$$p(\vec{x}|C_k) = \frac{1}{\left(2 \pi \right)^{n/2}} \frac{1}{\left|\vec{\Sigma}\right|^{1/2}} \exp \left( -\frac{1}{2} \left(\vec{x} - \vec{\mu}_k\right)^\intercal \vec{\Sigma}^{-1} \left(\vec{x} - \vec{\mu}_k\right) \right)\,,$$

where:

• $n$ is the dimension of the input vector $\vec{x}$,
• $\vec{\Sigma}$ is the covariance matrix, and
• $\vec{\mu}$ is the mean vector.

Note: here we have assumed that all classes share the same covariance matrix.

A logistic function or logistic curve is a common "S" shape (sigmoid curve), defined as

$$\sigma(x)={\frac {L}{1+\exp(-k(x-x_{0}))}}\,,$$

where:

• $x_0$: the x-value of the sigmoid's midpoint,
• $L$: the curve's maximum value, and
• k: the steepness of the curve.

In the case of two classes, this result is substituted into the logistic sigmoid function and reduces to

$$p(C_1|\vec{x}) = \sigma \left( \vec{w}^\intercal \vec{x} + w_0 \right)$$

were we have defined:

$$\vec{w} = \mathbf{\Sigma}^{-1} \left( \mathbf{\mu}_1 - \mathbf{\mu}_2 \right),$$$$w_0 = -\frac{1}{2} \vec{\mu}_1^\intercal \vec{\Sigma}^{-1} \vec{\mu}_1 + \frac{1}{2} \vec{\mu}_2^\intercal \vec{\Sigma}^{-1} \vec{\mu}_2 + \ln \frac{p(C_1)} {p(C_2)}$$

### What about $p(C_k)$?¶

• The class prior probabilities, $p(C_k)$, effectively act as a bias term.
• Note that we have yet to specify a model for these distributions.

If we are to use the result above, we will need to make another model assumption.

• We will assume that the class priors are modeled by a Bernoulli distribution with $p(C_1)=\gamma$ and $p(C_2)=1-\gamma$.

These results can be extended to the case of $K>2$ classes for which we obtain

$$a_k(\vec{x}) = \left[\vec{\Sigma}^{-1} \vec{\mu}_k \right]^\intercal \vec{x} - \frac{1}{2} \vec{\mu}_k^\intercal \vec{\Sigma}^{-1} \vec{\mu}_k + \ln p(C_k)$$

which is used in the first activation function provided at the begin of this section.

### What about the posterior class densities?¶

We have not formulated a complete model for the posterior class densities, in that we have not yet solved for the model parameters, $\vec{\mu}$ and $\vec{\Sigma}$.

We do that now using a maximum likelihood approach.

## Likelihood function¶

• A function of the parameters of a statistical model given some data.

The likelihood of a set of parameter values, $\theta$, given outcomes $x$, is the probability of those observed outcomes given those parameter values,

$$\mathcal{L}(\theta |x)=P(x|\theta)$$
• Frequently, the natural logarithm of the likelihood function, called the log-likelihood, is more convenient to work with.

## Maximum Likelihood Solution¶

Considering the case of two classes, $C_1$ and $C_2$, with

• Bernoulli prior distributions, $p(C_1)=\gamma$ and $p(C_2)=1-\gamma$, and
• Gaussian class-conditional density distributions $p(\vec{x}|C_k)$,
• assume we have a training data set, $\Psi$, with $m$ elements of the form
$$\Psi = \left\{\left\langle\vec{x}_i, t_i\right\rangle\right\}$$

where $t_i=0$ indicates that $\vec{x_i}$ is in class $C_1$ and $t_i=1$ indicates that $\vec{x}_i$ is in class $C_2$.

The likelihood function is then given by

$$p\left(\vec{t}, \vec{X} \mid \gamma, \vec{\mu}_1, \vec{\mu}_2, \vec{\Sigma}\right) = \prod_{i=1}^m \left[\gamma \mathcal{N} \left(\vec{x}_i \mid \mu_1, \vec{\Sigma}\right)\right]^{t_i} \left[\left(1-\gamma\right) \mathcal{N}\left(\vec{x}_i \mid \mu_2, \vec{\Sigma}\right)\right]^{1-t_i}\,.$$

Taking the derivate of this expression with respect to the various model parameters, $\gamma$, $\mu_1$, $\mu_2$, and $\vec{\Sigma}$, and setting it equal to zero, we obtain

$$\gamma = \frac{N_1}{N_1+N_2}$$

where $N_1$ is the number of training inputs in class $C_1$ and $N_2$ is the number in class $C_2$.

#### similarly...¶

$$\mu_2 = \frac{1}{N_2} \sum_{i=1}^m t_i \vec{x}_i\,,$$$$\mu_1 = \frac{1}{N_1} \sum_{i=1}^m (1-t_i) \vec{x}_i\,,\ \text{and}$$$$\vec{\Sigma} = \frac{1}{m}\left[ \sum_{i\in C_1} (\vec{x}_i-\mu_1)(\vec{x}_i-\mu_1)^T + \sum_{i\in C_2} (\vec{x}_i-\mu_2)(\vec{x}_i-\mu_2)^T \right]\,.$$

## Example¶

• We will choose some truth values for our parameters and use our generative model to generate synthetic data.
• We can then use that data as input to the maximum likelihood solution to see the estimates of the truth parameters.
• Let's use 1-D input for simplicity.
• We will be using a basic form of inverse transform sampling.
• Specifically, we wish for our training input data, $\vec{x}$, to be derived from a distribution modeled by the marginal distribution $p(\vec{x})$.
• To obtain this, we first formulate the cumulative distribution function, $CDF(\vec{X}) = \int_{-\infty}^{\vec{X}} p(\vec{x})d\vec{x}$ (note that the range of the CDF is $[0,1]$).
• To obtain appropriately distributed $\vec{x}$ values, we choose some value from a uniform distribution on $[0,1]$, say $y$, and find the value $\vec{X}$ such that $CDF(\vec{X})=y$.
• This value of $\vec{X}$ is our input $\vec{x}$.
• This approach requires us to find the inverse of the CDF, which we will do numerically.
• Once we have obtained a value for $\vec{x}$ we need to assign it to a particular class.

We will assume that the correct class is that for which the posterior probability $p(\left.C_k\right|\vec{x})$ is greatest, unless the difference between the two posterior probabilities is less than some minimum value in which case we will chose randomly - this will add some "noise" to our input training data.

Select truth data values, these will NOT be known to the training algorithm, they are only used in generating the sample data.

In [4]:
true_mu1 =-2.0
true_mu2 = 2.0
true_sigma = 2.0
true_gamma = 0.5

In [5]:
import scipy.integrate as sci_intgr
import scipy.optimize as sci_opt


Defining probability functions

In [6]:
def p_xCk(x, mu, sigma):
'Class conditional probability p(x|Ck)'
denom = math.sqrt(2.0 * math.pi * sigma)
arg = -0.5 * (x - mu) * (x - mu) / sigma
return math.exp(arg) / denom

In [7]:
def p_x(x, mu1, mu2, sigma, gamma):
'Marginal probability p(x)'
return gamma * p_xCk(x, mu1, sigma) + (1.0 - gamma) * p_xCk(x, mu2, sigma)

In [8]:
def p_Ckx(x, mu1, mu2, sigma, gamma):
'Posterior class probability vector (p(C_1|x), p(C_2|x))'
a = math.log(p_xCk(x, mu1, sigma)*gamma/(p_xCk(x,mu2,sigma)*(1-gamma)))
pc1 = 1.0/(1.0 + math.exp(-a))
return (pc1, 1.0 - pc1)

In [9]:
def cdf(x, mu1, mu2, sigma, gamma):
'Cumulative distribution function P(x<X)'
return sci_intgr.quad(func=p_x, a=-np.inf, b=x, args=(mu1, mu2, sigma, gamma))

In [10]:
def inv_cdf(y, mu1, mu2, sigma, gamma):
'Inverse of the CDF'
f = lambda x: cdf(x,mu1,mu2,sigma,gamma)[0] - y
return sci_opt.newton(f, 0)


Class conditional probabilities

In [11]:
domain = np.linspace(-5, 5, 100)

In [12]:
px_class1 = [p_xCk(x, true_mu1, true_sigma) for x in domain]
px_class2 = [p_xCk(x, true_mu2, true_sigma) for x in domain]

In [13]:
fig = plt.figure(figsize=(5,4))
plt.plot(domain, px_class1, label='Class 1')
plt.plot(domain, px_class2, label='Class 2')
plt.xlabel('$x$'); plt.ylabel('$p(x|C_k)$');plt.legend(bbox_to_anchor=(1.37,1), fancybox=True);
plt.title('Class conditional probability - $p(x|C_k)$');


Marginal distribution of $x$

In [14]:
px = [p_x(x, true_mu1, true_mu2, true_sigma, true_gamma) for x in domain]

In [15]:
fig = plt.figure(figsize=(5,4))
plt.plot(domain,px)
plt.xlabel('$x$');plt.ylabel('$p(x)$');plt.title('Marginal distribution - $p(x)$');


Posterior distribution

In [16]:
pc1x, pc2x = [], []
for x in domain:
pck = p_Ckx(x, true_mu1, true_mu2, true_sigma, true_gamma)
pc1x.append(pck[0]); pc2x.append(pck[1])

In [17]:
fig = plt.figure(figsize=(5,4))
plt.plot(domain, pc1x, label='Class 1')
plt.plot(domain, pc2x, label='Class 2')
plt.xlabel('$x$');plt.ylabel('$p(C_k|x)$');
plt.title('Posterior distributions - $p(C_1|x)$ and $p(C_2|x)$');


Or model is ready, let's generate some data and test it.

In [18]:
np.random.seed(123456789) # do not do this in real life
num_samples = 1000
x = np.zeros(num_samples)
t = np.zeros(num_samples)
pcx = np.zeros(num_samples)
n1 = 0
nae = 0
assignment_epsilon = 0.5


Assigning x to 1 for class 1 and 0 for class 2.

In [19]:
for i in range(num_samples):
rv = np.random.uniform()
x[i] = inv_cdf(rv, true_mu1, true_mu2, true_sigma, true_gamma)
pcx1 = p_Ckx(x[i], true_mu1, true_mu2, true_sigma, true_gamma)
pcx2 = pcx1[1]
pcx1 = pcx1[0]
#we don't want a perfect dividing line for our domain, otherwise why would we need a learning algorithim?
if math.fabs(pcx2-pcx1) <= assignment_epsilon:
nae = nae + 1
if np.random.uniform() <= 0.5:
t[i] = 1
n1 = n1 + 1
else:
t[i] = 0
elif pcx1 > pcx2:
t[i] = 1
n1 = n1 + 1
else: t[i]=0


### Plot the simulated data¶

In [20]:
fig = plt.figure(figsize=(5,4))
plt.scatter(x, t, marker='.', alpha=0.1)
plt.axvline(0, linestyle='--')
plt.xlabel('$x$');plt.ylabel('Class ($t$)'); plt.title('Simulated training data');


## Estimating the parameters¶

In [21]:
n2 = num_samples - n1
e_gamma = n1/num_samples

In [22]:
e_mu1 = 0
for x_i, t_i in zip(x,t):
if t_i == 1:
e_mu1 += x_i
# e_mu1 += x_i*t_i
e_mu1 /= n1

In [23]:
e_mu2 = 0
for x_i, t_i in zip(x,t):
if t_i == 0:
e_mu2 += x_i
# e_mu2 += x_i*(1-t_i)
e_mu2 /= n2

In [24]:
def Sn(Z, ck, mu=(e_mu1,e_mu2)):
idx = 1-int(ck)
return (Z[0]-mu[idx]) * (Z[0]-mu[idx])

In [25]:
e_sigma = 0
for x_i, t_i in zip(x,t):
e_sigma += Sn((x_i, t_i), t_i)
e_sigma /= float(num_samples)

In [26]:
print('The number of inputs in C1 is {0}'.format(n1))
print('The number of inputs that were assigned based on the assignment factor is {0}'.format(nae))
print('True model parameters: gamma={0}, µ1={1}, µ2={2}, ∑={3}'.format(true_gamma,
true_mu1, true_mu2,
true_sigma))
print('Estimated model parameters: gamma={0}, µ1={1}, µ2={2}, ∑={3}'.format(e_gamma, e_mu1,
e_mu2, e_sigma))

The number of inputs in C1 is 505
The number of inputs that were assigned based on the assignment factor is 121
True model parameters: gamma=0.5, µ1=-2.0, µ2=2.0, ∑=2.0
Estimated model parameters: gamma=0.505, µ1=-2.069287615754436, µ2=2.095874429059065, ∑=1.7242111261175646


# Probabilistic discriminative models¶

Probablistic discriminative models assume the same generalized linear model

$$y(\phi(\vec{x})) = f(\vec{w}^\intercal\phi(\vec{x}))$$

as in the probabilistic generative models.

• Instead of formulating models for the class-conditional densities, $p\left(\phi(\vec{x})\right|\left.C_k\right)$, and the class priors, $p(C_k)$,
• the discriminative approach explicitly models the class posterior probabilities, $p\left(C_k\right|\left.\phi(\vec{x})\right)$ with model parameters $\vec{w}$.
• As in the probabilistic generative approach, maximum likelihood is used to estimate the model parameters given some training data set, $\Psi$.

The key difference is the form of the likelihood function.

• In the probabilistic generative case, the likelihood function is a function of the joint probability, $p\left(\phi(\vec{x}),C_k\right)=p\left(\phi(\vec{x})\right|\left.C_k\right)p(C_k)$.
• In the probabilistic discriminative approach, the likelihood function is a function of the conditional class posteriors, $p\left(C_k\right|\left.\phi(\vec{x})\right)$ only.

## Logistic Regression¶

• Logistic regression is one specific example of discriminative modeling, for the case of two classes.
• It assumes a model for the class posterior probabilities, $p(C_k|\phi(\vec{x}))$, in the form of the logistic sigmoid
$$p\left(C_1\right|\left.\phi(\vec{x})\right) = f(a) = \sigma\left(\vec{w}^\intercal\phi(\vec{x})\right)$$

with

$$p\left(C_2\right|\left.\phi(\vec{x})\right) = 1 - p\left(C_1\right|\left.\phi(\vec{x})\right).$$
• We apply maximum likelihood to obtain the model parameters.
• Assume that our training set is of the form $\Psi=\left\{\phi(\vec{x}_i),t_i\right\}$ where $t_i \in \{0,1\}$, and $i=1,\ldots, m$.
• The likelihood function of the training data is then $$p(\Psi|\vec{w}) = \prod_{i=1}^m \sigma(\vec{w}^\intercal \phi(\vec{x}_i))^{t_i} (1 - \sigma(\vec{w}^\intercal \phi(\vec{x}_i)))^{(1-t_i)}$$

Defining the error function, $E(\vec{w})$, as the negative of the log-likelihood function, and taking the gradient with respect to $\vec{w}$, we obtain $$\bigtriangledown E(\vec{w}) = \sum_{i=1}^m \left[\sigma(\vec{w}^\intercal \phi(\vec{x}_i)) - t_i\right] \phi(\vec{x}_i).$$

• this error function looks the same as that obtained for linear regression under the assumption of a Gaussian noise model which had a closed form solution.
• the nonlinearity of the sigmoid function, $\sigma\left(\vec{w}^\intercal \phi(\vec{x}_i)\right)$ prevents a closed form solution in the logistic regression problem.
• We must apply an iterative method to obtain a numerical solution for the parameters, $\vec{w}$.

Here we will consider the Newton-Raphson method for which minimizing the error function takes the form

$$\vec{w}^{(\tau+1)} = \vec{w}^{(\tau)} - \vec{H}^{-1}\bigtriangledown E(\vec{w}),$$

where $\vec{H}$ is the Hessian matrix composed of the second derivatives of the error function

$$\vec{H} = \bigtriangledown \bigtriangledown E(\vec{w}) = \Phi^\intercal \vec{R} \Phi,$$

where $\Phi$ is the $n \times m$ design matrix whose $n$-th row is given by $\phi(\vec{x_n})^\intercal$ and $\vec{R}$ is an $n \times n$ diagonal matrix with elements.

$$R_{i,i} = \sigma(\vec{w}^\intercal \phi(\vec{x}_i)) \left[1-\sigma(\vec{w}^\intercal \phi(\vec{x}_i))\right].$$

This can be reduced to a form equivalent to that of locally weighted linear regression as follows

$$\vec{w}^{(\tau+1)} = \left( \Phi^T \vec{R} \Phi \right)^{-1} \Phi^T \vec{R} \vec{z}$$

where $\vec{z}$ is an $n$-dimensional vector defined by

$$\vec{z} = \Phi \vec{w}^{(\tau)} - \vec{R}^{-1}(\vec{y} - \vec{t})$$

## Example 2

Let's consider an example with two classes and 2D input, $\vec{x}_n = (x_n^{(1)},x_n^{(2)})$.

• As an experiment, you can try increasing the number of training points, N.
• Eventually, the training points will overlap so that it will not be possible to completely seperate them with the transformation provided.
In [27]:
# preparing training dataset
np.random.seed(123456789) # fixing for reproducubility
N = 100 #number of data points
D = 2   #dimension of input vector
t = np.zeros(N) #training set classifications
X = np.zeros((N,D)) #training data in input space
sigma = .25
mu0 = 0.0
mu1 = 1.0


### Generating test data¶

• Pick a value from a uniform distribution on $[0,1]$.
• If it is less than 0.5, assign class 1 and pick $x_1, x_2$ from a $\mathcal{N}(\mu_0,\sigma)$
• otherwise assign class 2 and pick $x_1,x_2$ from $\mathcal{N}(\mu_1,\sigma)$
In [29]:
for i in range(N):
#choose class to sample for
fac = 1
if np.random.rand() <= 0.5:
thismu = mu0
t[i] = 1
else:
thismu = mu1
t[i] = 0
if np.random.rand() < 0.5: fac = -1
X[i,0] = fac * np.random.normal(thismu, sigma)
X[i,1] = fac * np.random.normal(thismu, sigma)

In [30]:
fig = plt.figure(figsize=(5, 5))
ax = fig.gca()
ax.set_xlabel('$x_1$')
ax.set_ylabel('$x_2$')
ax.set_title('Training data set')
create_scatter(X, t, ax)


The training data does not have a linear boundary in the original input space.

• So lets apply a transformation, $\phi()$ (function phi(...)) to try to make it linearly separable
• Note: This transformation is not the only one that works.
• For example try switching the values of $\mu_1$ and $\mu_2$.
• The result will be a different mapping that is still linearly separable.
In [31]:
def phi(x, mu, sigma):
detSigma = np.linalg.det(sigma)
fac = math.pow(2 * math.pi, len(mu) / 2.0) * math.sqrt(detSigma)
arg = -0.5 * np.dot((x - mu).T, np.dot(np.linalg.inv(sigma), x - mu))
return math.exp(arg) / fac

In [32]:
phiX = np.zeros((N,D))
MU1 = np.ones(D)*mu0
MU2 = np.ones(D)*mu1
SIGMA = np.diag(np.ones(D))*sigma
for i in range(N):
phiX[i,0] = phi(x=X[i,:], mu=MU2, sigma=SIGMA)
phiX[i,1] = phi(x=X[i,:], mu=MU1, sigma=SIGMA)

In [33]:
fig = plt.figure(figsize=(5,5)); ax =fig.gca()
ax.set_xlabel('$x_1$'); ax.set_ylabel('$x_2$')
create_scatter(phiX,t,ax)


Now, lets apply machine learning to determine the boundary!

• We will assume M = 3, i.e. that there are 3 free parameters,
• that is $\vec{w} = [w_0, w_1, w_2]^\intercal$ and,
• phi_n = [1, phiX[0], phiX[1]].
In [34]:
M = 3
Phi = np.ones((N,M))
Phi[:,1] = phiX[:,0]
Phi[:,2] = phiX[:,1]
w = np.zeros(M)
R = np.zeros((N,N))
y = np.zeros(N)

In [35]:
def sigmoid(a):
return 1.0 / (1.0 + math.exp(-a))

def totalErr(y,t):
e = 0.0
for i in range(len(y)):
if t[i] > 0:
e += math.log(y[i])
else:
e += math.log(1.0 - y[i])
return -e


Starting Newton-Raphson.

• As a stopping criteria we will use a tolerance on the change in the error function and a maximum number of iterations
In [36]:
max_its = 100
tol = 1e-2
w0 = [w[0]]
w1 = [w[1]]
w2 = [w[2]]
err = []
error_delta = 1 + tol
current_error = 0
idx = 0

In [37]:
from functools import reduce

In [38]:
while math.fabs(error_delta) > tol and idx < max_its:
#update y & R
for i in range(N):
y[i] = sigmoid(reduce(lambda accum, Z: accum + Z[0]*Z[1], zip(w, Phi[i,:]), 0))
R[i,i] = y[i] - y[i]*y[i]
#update w
z = np.dot(Phi,w) - np.dot(np.linalg.pinv(R),y-t)
term_1 = np.linalg.pinv(np.dot(np.dot(Phi.T,R),Phi))
term_2 = np.dot(np.dot(term_1, Phi.T),R)
w = np.dot(term_2, z)
w0.append(w[0])
w1.append(w[1])
w2.append(w[2])
idx += 1
temp = totalErr(y,t)
error_delta = current_error - temp
current_error = temp
err.append(error_delta)

In [39]:
print('The total number of iterations was {0}'.format(idx))
print('The total error was {0}'.format(current_error))
print('The final change in error was {0}'.format(error_delta))
print('The final parameters were {0}'.format(w))

The total number of iterations was 13
The total error was 0.003482221426895694
The final change in error was 0.00577180168397616
The final parameters were [ -17.844  -14.062  163.065]


Our decision boundary is now formed by the line where $\sigma(a) = 0.5$, i.e. where $a = 0$, which for this example is where $\phi_2 = -\frac{w_1}{w_2}\phi_1$, i.e. where $\vec{w} \cdot\vec{\phi} = 0.5$.

In [40]:
fig = plt.figure(figsize=(5,5)); ax =fig.gca()
ax.set_xlabel('$x_1$'); ax.set_ylabel('$x_2$')
create_scatter(phiX,t,ax)

bdryx = (-0.2,1.1)
bdryy = (-(w[0]+w[1]*bdryx[0])/w[2], -(w[0]+w[1]*bdryx[1])/w[2])
ax.plot(bdryx, bdryy, linestyle='--');

In [41]:
fig = plt.figure(figsize=(5,5))
plt.plot(w0, 'bo-', label='$w_0$')
plt.plot(w1, 'rx-', label='$w_1$')
plt.plot(w2, 'gs-', label='$w_2$')
plt.plot(err, 'm*-', label='error delta')
plt.legend(loc='upper left', frameon=True)
plt.xlabel('Newton-Raphson iterations');