def print_regresion_model_chart(self, beta_1, beta_2):
self.read_source_data()
lineChart = LineChart()
lineChart.xdata, lineChart.ydata = (self._df["Year"].values,
self._df["Value"].values)
print('****************')
print(lineChart.xdata, lineChart.ydata)
print('****************')
# Normalización
self.get_normalized_data(lineChart)
print('****************')
print(lineChart.xdata, lineChart.ydata)
print('****************')
x = np.linspace(1960, 2015, 55)
lineChart.x = x / max(x)
lineChart.plt.figure(figsize=(8, 5))
lineChart.y = self.sigmoid(lineChart.x, *self._popt)
lineChart.plt.plot(lineChart.xdata,
lineChart.ydata,
'ro',
label='data')
lineChart.plt.plot(lineChart.x,
lineChart.y,
linewidth=3.0,
label='fit')
lineChart.plt.legend(loc='best')
lineChart.set_labels('Year', 'GDP')
self.calculate_model_accuracy(lineChart)
return lineChart.print_to_imgb64()
def no_lineal_demo(self):
self.read_source_data()
lineChart = LineChart()
lineChart.xdata, lineChart.ydata = (self._df["Year"].values,
self._df["Value"].values)
lineChart.plt.figure(figsize=(8, 5))
lineChart.plt.plot(lineChart.xdata, lineChart.ydata, 'ro')
lineChart.set_labels('Year', 'GDP')
return lineChart.print_to_imgb64()
def print_sigmoid_chart(self, beta_1, beta_2):
self.read_source_data()
lineChart = LineChart()
lineChart.xdata, lineChart.ydata = (self._df["Year"].values,
self._df["Value"].values)
#función logística
Y_pred = self.sigmoid(lineChart.xdata, beta_1, beta_2)
#predicción de puntos
lineChart.plt.plot(lineChart.xdata, Y_pred * 15000000000000.)
lineChart.plt.plot(lineChart.xdata, lineChart.ydata, 'ro')
return lineChart.print_to_imgb64()