sigma = possible_values[j]
svm = SVC(kernel='rbf', C=C_value, gamma=(1 / (2 * sigma**2)))
svm.fit(X, y.ravel())
current_score = true_score(
Xval, yval, svm
) #calcula el score con los ejemplos de validacion (mayor score, mejor es el svm)
if current_score > max_score:
max_score = current_score
best_svm = svm
selected_C = C_value
selected_Sigma = sigma
return best_svm, selected_C, selected_Sigma
X, y = Data_Management.load_csv_svm("pokemon.csv", [feature1, feature2])
X, mu, sigma = Normalization.normalize_data_matrix(X)
X, y, trainX, trainY, validationX, validationY, testingX, testingY = Data_Management.divide_legendary_groups(
X, y)
max_score = float("-inf")
best_svm = None
for i in range(NUM_TRIES):
#THIS IS GIVING THE SAME RESULT, ALWAYS (MAYBE SELECT C AND SIGMA RANDOMLY)
seed = np.random.seed()
current_svm, C, s = eleccion_parametros_C_y_Sigma(trainX, trainY,
validationX, validationY,
mu, sigma)
current_score = true_score(testingX, testingY, current_svm)
draw_decisition_boundary(testingX, testingY, current_svm, current_score,
#set the labels to non-normalized values
figure.canvas.draw()
labels = [item for item in plt.xticks()[0]]
for i in range(len(labels)):
labels[i] = int(round((labels[i] * sigma[0, 0]) + mu[0, 0], -1))
ax.xaxis.set_ticklabels(labels)
labels = [item for item in plt.yticks()[0]]
for i in range(len(labels)):
labels[i] = int(round((labels[i] * sigma[0, 1]) + mu[0, 1], -1))
ax.yaxis.set_ticklabels(labels)
plt.show()
X, y = Data_Management.load_csv_svm("pokemon.csv", ["base_egg_steps", "base_happiness"])
nX, ny, ntrainX, ntrainY, nvalidationX, nvalidationY, ntestingX, ntestingY = Data_Management.divide_legendary_groups(X, y)
#normalize
p, X = polinomial_features(X, 5)
X, mu, sigma = Normalization.normalize_data_matrix(X[:, 1:])
#ssX = Data_Management.add_column_left_of_matrix(X)
X, y, trainX, trainY, validationX, validationY, testingX, testingY = Data_Management.divide_legendary_groups(X, y)
num_entradas = np.shape(X)[1]
num_ocultas = 25
num_etiquetas = 1
true_score_max = float("-inf")
falsePositives += 1
elif checker[i] == 0 and y[i] == 1:
falseNegatives += 1
if truePositives == 0:
return 0
recall = (truePositives / (truePositives + falseNegatives))
precision = (truePositives / (truePositives + falsePositives))
score = 2 * (precision * recall / (precision + recall))
return score
graphic_attr_names = ["capture_rate", "base_egg_steps"]
X, y = Data_Management.load_csv_svm("pokemon.csv", graphic_attr_names)
X, mu, sigma = Normalization.normalize_data_matrix(X)
X, y, trainX, trainY, validationX, validationY, testingX, testingY = Data_Management.divide_legendary_groups(
X, y)
#-------------------------------------------------------------------------------------------------
allMaxPercent = []
allMaxElev = []
allMaxPoly = []
allMaxThetas = []
Xused = validationX
Yused = validationY
plot_decision_boundary(X,
y,
model,
self.epoch_count,
self.count,
cmap='RdBu')
score, acc = model.evaluate(X, y, verbose=0)
print("error " + str(score) + ", accuracy " + str(acc))
self.count = self.count + 1
self.epoch_count = self.epoch_count + 1
return self.epoch_count
if __name__ == "__main__":
X, y = datasets.make_moons(n_samples=1000, noise=0.1, random_state=0)
X, y = Data_Management.load_csv_svm("pokemon.csv",
['weight_kg', 'height_m'])
X, y, trainX, trainY, validationX, validationY, testingX, testingY = Data_Management.divide_legendary_groups(
X, y)
y = y.ravel()
trainY = trainY.ravel()
# Create a directory where image will be saved
os.makedirs("images_new", exist_ok=True)
# Define our model object
model = Sequential()
# Add layers to our model
model.add(
Dense(units=25,
input_shape=(2, ),