def getPredictedPriceNormalized(self):
# get model first
self.getModelFromFilePath(self, self.file)
input_features = self.df.iloc[:, [2, 3]].values
input_data = input_features
predicted_value = self.model.predict(self.X_test)
plt.figure(figsize=(100, 40))
plt.plot(predicted_value, color='red')
plt.plot(input_data[self.lookback:self.test_size + (2 * self.lookback),
1],
color='green')
plt.title("Opening price of stocks sold")
plt.xlabel("Time (latest-> oldest)")
plt.ylabel("Stock Opening Price")
plt.show()
self.sc.inverse_transform(input_features[self.lookback:self.test_size +
(2 * self.lookback)])
return predicted_value
import csv
open_file = open("sitka_weather_07-2018_simple.csv", "r")
csv_file = csv.reader(open_file, delimiter=",")
header_row = next(csv_file)
'''
print(header_row)
for index, column_header in enumerate(header_row):
print(index,column_header)
'''
highs = []
for row in csv_file:
highs.append(int(row[5]))
print(highs)
import matplotlib.pyploy as plt
plt.plot(highs, c="red")
plt.title("Daily High Temp, July 2018", fontsize=16)
plt.xlabel("")
plt.ylabel("Temperature (F)", fontsize=16)
plt.tick_params(axis="both", which="major", labelsize=16)
plt.show()
ret, thresh1 = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY)
ret, thresh2 = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY_INV)
ret, thresh3 = cv2.threshold(img, 127, 255, cv2.THRESH_TRUNC)
ret, thresh4 = cv2.threshold(img, 127, 255, cv2.THRESH_TOZERO)
rec, thresh5 = cv2.threshold(img, 127, 255, cv2.THRESH_TOZERO_INV)
titles = [
'original image', 'binary', 'binary_inv', 'trunc', 'tozero', 'tozero_inv'
]
images = [img, thresh1, thresh2, thresh3, thresh4, thresh5]
for i in xrange(6):
plt.subplot(2, 3, i + 1), plt.imshow(image[i], 'gray')
plt.title(titles[i])
plt.xticks([]), plt.yticks([])
plt.show()
#adaptive thresh
img = cv2.imread('dave.jpg', 0)
img = cv2.medianBlur(img, 5)
ret, th1 = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY)
th2 = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_MEAN_C,
cv2.THRESH_BINARY, 11, 2)
th3 = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
cv2.THRESH_BINARY, 11, 2)
low = int(row[5])
current_date= datetime.strptime(row[2],'%Y-%m-%d')
except ValueError:
print(f"Missing data for {current_date}")
else:
lows.append(int(row[5]))
highs.append(int(row[4]))
dates.append(current_date)
import matplotlib.pyploy as plt
fig= plt.figure()
plt.plot(dates, highs, c="red",alpha=0.5)
plt.plot(dates, lows, c="blue", aplha=0.5)
plt.title("Daily high and low temperatures- 2018\nDeath Valley", fontsize=16)
plt.xlabel("", fontsize=12)
plt.fill_between(dates, highs, lows, facecolor= 'blue', alpha=0.1)
fig.autofmt_xdate()
plt.ylabel("Temperature (F)", fontsize=16)
plt.tick_params(axis="both", labelsize=16)
plt.show()
import csv
import matplotlib.pyploy as plt
games = []
record = []
wins = 0
f = open('cardinals34.csv')
for row in csv.reader(f):
if not row[0].isdigit():
continue
if row[6].startswith('W') and row[13] == "Dean":
wins += 1
games.append(int(row[0]))
record.append(wins)
plt.title('Dean Brothers progress toward 49 wins')
plt.xlabel('Game number')
plt.ylabel('Win count')
plt.plot(games, record, 'r+')
plt.savefig(games.pdf)
print("eigenvectors: \n", eigen_vectors)
for val in eigen_values:
print(val)
variance_explain = [(i/sum(eigen_values))*100 for i in eigen_values]
cumulative_var = np.cumsum(variance_explained)
cumulative_var
sns.lineplot(x = [1,2,3,4]
plt.xlabel("number of components")
plt.ylabel("cumulative variance")
plt.title("explain variance ratio")
plt.show()
#select eigenvectors and compute PCA
eigen_vectors
projection_matrix = (eigen_vectors.T[:][:])[:2].T
print ("projection_matrix :\n", projection_matrix)
X_pca = X.dot(projection_matrix)
for species in ('Iris-setosa','Iris-versicolor', 'Iris-virginica'):
sns.scatterplot(X_pca[y=species,0],
X_pca[y])