Commit 9f90b40a authored by Your Name's avatar Your Name
Browse files

adjusted regression model

parent 7fcf1d76
This diff is collapsed.
This diff is collapsed.
......@@ -77,6 +77,19 @@ def calculate_empties(df, threshold):
empties_indices.pop(idx)
return empties_indices
def calculate_empties_adaptive(df, threshold):
top_thresh = df.inter_pol.mean() + (df.inter_pol.std() * 0.75)
empties_indices = []
for idx,val in enumerate(df['inter_pol']):
if idx != 0 and (df['inter_pol'][idx-1]*threshold < (df['inter_pol'][idx])) and (df['inter_pol'][idx] > top_thresh):
empties_indices.append(idx)
# filter out double values
for idx,val in enumerate(empties_indices):
if idx !=0 and (empties_indices[idx] - empties_indices[idx-1]) < 3:
empties_indices.pop(idx)
return empties_indices
# function calculation empties of container for cluster 0
def calculate_empties_0(df, threshold):
empties_indices = []
......
......@@ -81,7 +81,7 @@ def predictValues_clust0(input):
pred_list = list()
temp_list = input['inter_pol'].tolist()
emptie_checkpoints = preprocessing.calculate_empties_0(input, 1.01)
emptie_checkpoints = preprocessing.calculate_empties_adaptive(input, 1.015)
for k in range(emptie_checkpoints[0]):
y = -3.62261628 * k + temp_list[0]
......@@ -114,7 +114,7 @@ def predictValues_clust1(input):
pred_list = list()
temp_list = input['inter_pol'].tolist()
emptie_checkpoints = preprocessing.calculate_empties_1(input, 1.17)
emptie_checkpoints = preprocessing.calculate_empties_adaptive(input, 1.015)
for k in range(emptie_checkpoints[0]):
y = -3.63041747 * k + temp_list[0]
......@@ -146,7 +146,7 @@ def predictValues_clust2(input):
pred_list = list()
temp_list = input['inter_pol'].tolist()
emptie_checkpoints = preprocessing.calculate_empties_2(input, 1.16)
emptie_checkpoints = preprocessing.calculate_empties_adaptive(input, 1.015)
for k in range(emptie_checkpoints[0]):
y = -3.62261628 * k + temp_list[0]
......@@ -204,13 +204,13 @@ def linearRegressionPlot_pred(input, cluster):
if cluster == 0:
predicted = predictValues_clust0(input)
empties = preprocessing.calculate_empties_0(input, 1.10)
empties = preprocessing.calculate_empties_adaptive(input, 1.015)
elif cluster == 1:
predicted = predictValues_clust1(input)
empties = preprocessing.calculate_empties_1(input, 1.17)
empties = preprocessing.calculate_empties_adaptive(input, 1.015)
else:
predicted = predictValues_clust2(input)
empties = preprocessing.calculate_empties_2(input, 1.16)
empties = preprocessing.calculate_empties_adaptive(input, 1.015)
plt.plot(predicted)
plt.legend(['Preprocessed', 'Predictions'], loc='upper left')
......
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