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<|repo_name|>SoyeonKim2018/CS488-Machine-Learning<|file_sep|>/hw3/hw3_4.py
import numpy as np
import random
import math
import pandas as pd
from sklearn import preprocessing
from sklearn.decomposition import PCA # feature normalization
def normalize(x):
x = preprocessing.scale(x)
return x # load data
train = np.loadtxt('hw3_4_train.csv', delimiter=',')
test = np.loadtxt('hw3_4_test.csv', delimiter=',') # normalize features
x_train = train[:, :-1]
y_train = train[:, -1]
x_test = test[:, :-1]
y_test = test[:, -1] x_train = normalize(x_train)
x_test = normalize(x_test) # initialize parameters
K = len(np.unique(y_train))
N = len(y_train)
w = np.zeros((K,x_train.shape[1]))
b = np.zeros((K)) # gradient descent parameters
alpha = .01
lambda_ = .01 # training using gradient descent
for i in range(100):
for j in range(N):
# get class label (1 or -1) for current example (j)
y_j = y_train[j] # find w_i * x_j + b_i for each i (class)
z_ji = np.dot(w.T,x_train[j,:]) + b # find sum over all classes except y_j
s_ji = np.sum(np.exp(z_ji[np.arange(K)!=y_j-1])) for k in range(K):
if k == y_j-1:
w[k,:] += alpha*(x_train[j,:] - (s_ji/(s_ji+1))*w[k,:]) - alpha*lambda_*w[k,:]
b[k] += alpha*(1 - (s_ji/(s_ji+1)))- alpha*lambda_*b[k]
else:
w[k,:] -= alpha*((s_ji/(s_ji+1))*w[k,:]) - alpha*lambda_*w[k,:]
b[k] -= alpha*(s_ji/(s_ji+1)) - alpha*lambda_*b[k] # compute accuracy using testing set
correct_predictions = []
for i in range(len(y_test)):
z_test_i = np.dot(w.T,x_test[i,:]) + b s_test_i = np.sum(np.exp(z_test_i[np.arange(K)!=y_test[i]-1])) prediction = np.argmax(z_test_i)
if prediction+1 == y_test[i]:
correct_predictions.append(1)
else:
correct_predictions.append(0) print("accuracy: " + str(sum(correct_predictions)/len(correct_predictions))) <|repo_name|>SoyeonKim2018/CS488-Machine-Learning<|file_sep|>/hw3/hw3_6.py
import numpy as np
import random
import math
import pandas as pd def sigmoid(x):
return np.exp(x)/(np.exp(x)+1) def normalize(x):
x_norm = x-x.mean(axis=0)
x_norm /= x_norm.std(axis=0)
return x_norm # load data
train = np.loadtxt('hw3_6_train.csv', delimiter=',')
test = np.loadtxt('hw3_6_test.csv', delimiter=',') x_train = train[:, :-1]
y_train = train[:, -1]
x_test = test[:, :-1]
y_test = test[:, -1] x_train_normed = normalize(x_train)
x_test_normed = normalize(x_test) K=10
N=len(y_train)
d=x_train.shape[1] # initialize weights
w=np.random.randn(d,K)
b=np.random.randn(K) alpha=0.01
lamb=0 for i in range(100):
for j in range(N):
z=np.dot(w.T,x_train_normed[j,:])+b
y_hat=sigmoid(z) error=y_hat-y_train[j]
w=w-alpha*(np.dot(error,y_hat*(1-y_hat))*x_train_normed[j,:].reshape(d,1)).T-lamb*w
b=b-alpha*np.sum(error*y_hat*(1-y_hat)) z=np.dot(w.T,x_test_normed.T)+b.reshape(-1,1)
y_hat=sigmoid(z) correct_predictions=[]
for i in range(len(y_test)):
if y_hat[i]>0.5:
prediction=1
else:
prediction=0
if prediction==y_test[i]:
correct_predictions.append(1)
else:
correct_predictions.append(0) print("accuracy: "+str(sum(correct_predictions)/len(correct_predictions)))<|file_sep|># CS488-Machine-Learning<|repo_name|>SoyeonKim2018/CS488-Machine-Learning<|file_sep|>/hw4/hw4.py
import numpy as np
import pandas as pd def sigmoid(x):
return np.exp(x)/(np.exp(x)+1) def softmax(z):
return np.exp(z)/np.sum(np.exp(z), axis=0) def cross_entropy(p,q):
return -np.sum(p*np.log(q)) def relu(x):
return np.maximum(0,x) class MLP: def __init__(self,d,h,k): self.d=d # input dimensionality
self.h=h # hidden layer dimensionality
self.k=k # output dimensionality self.W_12=np.random.randn(self.d,self.h) # W12 : d x h weight matrix between input layer (d units) & hidden layer (h units)
self.b_12=np.random.randn(self.h) # b12 : h dimensional vector representing biases for hidden layer self.W_23=np.random.randn(self.h,self.k) # W23 : h x k weight matrix between hidden layer & output layer
self.b_23=np.random.randn(self.k) # b23 : k dimensional vector representing biases for output layer def forward(self,x): self.z_12=np.dot(self.W_12.T,x)+self.b_12.reshape(-1,1) # z12 : h dimensional vector representing activations at hidden layer
self.a_12=sigmoid(self.z_12) # a12 : h dimensional vector representing activations at hidden layer after sigmoid nonlinearity self.z_23=np.dot(self.W_23.T,self.a_12)+self.b_23.reshape(-1,1) # z23 : k dimensional vector representing activations at output layer
self.a_23=softmax(self.z_23) # a23 : k dimensional vector representing activations at output layer after softmax nonlinearity def backward(self,x,y,alpha,lamb): delta_k=self.a_23-y.reshape(-1,1) # delta_k : k dimensional vector representing difference between predicted label distribution & true label distribution
delta_h=self.W_23*delta_k*sigmoid(self.z_12)*(sigmoid(-self.z_12)) # delta_h : h dimensional vector representing difference between predicted activations at hidden layer & true activations at hidden layer self.W_23-=alpha*(np.dot(delta_k,self.a_12.T)+lamb*self.W_23)
self.b_23-=alpha*(delta_k+lamb*self.b_23.reshape(-1)) self.W_12-=alpha*(np.dot(delta_h,x.T)+lamb*self.W_12)
self.b_12-=alpha*(delta_h+lamb*self.b_12.reshape(-1)) def train(self,data_x,data_y,alpha,lamb,num_iters): num_data=data_x.shape[0] # number of training examples for i in range(num_iters): idx=random.randint(0,num_data-1) # randomly select one training example self.forward(data_x[idx])
self.backward(data_x[idx],data_y[idx],alpha,lamb) def predict(self,data_x): num_data=data_x.shape[0] # number of testing examples
y_pred=np.zeros(num_data,dtype=int) for i in range(num_data):
self.forward(data_x[i])
y_pred[i]=np.argmax(self.a_23) return y_pred if __name__=="__main__": data=pd.read_csv("mnist.csv",header=None).values # read csv file containing MNIST data
data=data/255 # scale data between [0..255] down to [0..1] num_data=data.shape[0] # number of examples
dim_data=data.shape[1]-10 # dimensionality of input images X=data[:,:dim_data] # input data
Y=data[:,-10:].argmax(axis=1) # true labels idx=random.sample(range(num_data),k=int(num_data*0.8)) # select first ~80% indices as training set
X_train=X[idx]
Y_train=Y[idx] idx=random.sample(list(set(range(num_data))-set(idx)),k=int(num_data*0.05)) # select ~5% indices as validation set
X_valid=X[idx]
Y_valid=Y[idx] idx=list(set(range(num_data))-set(idx)-set(idx)) # select remaining ~15% indices as testing set
X_test=X[idx]
Y_test=Y[idx] hid_dim=[25] #[50] #[25] #[50] for h_dim in hid_dim: mlp=MLP(dim_data,h_dim,Y_train.max()+1)
mlp.train(X_train,Y_train,alpha=0.001,lamb=0,num_iters=10000) Y_valid_pred=mlp.predict(X_valid)
valid_accuracy=sum(Y_valid==Y_valid_pred)/len(Y_valid)*100 print("Validation Accuracy with {} Hidden Units: {}".format(h_dim,np.round(valid_accuracy),4)) Y_test_pred=mlp.predict(X_test)
test_accuracy=sum(Y_test==Y_test_pred)/len(Y_test)*100 print("Test Accuracy with {} Hidden Units: {}".format(h_dim,np.round(test_accuracy),4)) <|file_sep|># Machine Learning @ Georgia Tech CS Machine Learning course at Georgia Tech CS taken during Spring semester. ### Instructor: [Girish Chowdhary](https://www.cc.gatech.edu/~girish/) ### TA: [Vikas Agarwal](http://www.cs.gatech.edu/faculty/vagarwal) ### GitHub repo link: https://github.com/SoyeonKim2018/CS488-Machine-Learning <|repo_name|>SoyeonKim2018/CS488-Machine-Learning<|file_sep|>/hw3/hw3.py
import numpy as np
import random
import math def normalize(x):
x_norm=x-x.mean(axis=0)
x_norm/=x_norm.std(axis=0)
return x_norm def dot_product(v,w):
dot_sum=0
for i in range(len(v)):
dot_sum+=v[i]*w[i]
return dot_sum def compute_weights(data,label,alpha,lamb,num_iters):
num_examples=len(label)
dimensionality=len(data[0]) w=np.zeros(dimensionality) for iter_num in range(num_iters):
shuffle_index=list(range(num_examples))
random.shuffle(shuffle_index) return w if __name__=="__main__":
data=[]
label=[] with open("hw3.csv") as f:
for line in f.readlines():
line=line.split(",")
data.append(line[:-5])
label.append(int(line[-5]))
data=np.array(data,dtype=float)
label=np.array(label,dtype=int) data_normalized=normalize(data) alpha=.01
lamb=.01 num_iters=100000 w_optimal=compute_weights(data_normalized,label,alpha