I have no idea what I'm doing here.

serie1/iris.txt

+"Sepal.Length"	"Sepal.Width"	"Petal.Length"	"Petal.Width"	"Species"	"Sepal.Area"	"Petal.Area"	"Sepal.Ratio"	"Petal.Ratio"
+5.1	3.5	1.4	0.2	"setosa"	17.85	0.28	1.45714285714286	7
+4.9	3	1.4	0.2	"setosa"	14.7	0.28	1.63333333333333	7
+4.7	3.2	1.3	0.2	"setosa"	15.04	0.26	1.46875	6.5
+4.6	3.1	1.5	0.2	"setosa"	14.26	0.3	1.48387096774194	7.5
+5	3.6	1.4	0.2	"setosa"	18	0.28	1.38888888888889	7
+5.4	3.9	1.7	0.4	"setosa"	21.06	0.68	1.38461538461538	4.25
+4.6	3.4	1.4	0.3	"setosa"	15.64	0.42	1.35294117647059	4.66666666666667
+5	3.4	1.5	0.2	"setosa"	17	0.3	1.47058823529412	7.5
+4.4	2.9	1.4	0.2	"setosa"	12.76	0.28	1.51724137931034	7
+4.9	3.1	1.5	0.1	"setosa"	15.19	0.15	1.58064516129032	15
+5.4	3.7	1.5	0.2	"setosa"	19.98	0.3	1.45945945945946	7.5
+4.8	3.4	1.6	0.2	"setosa"	16.32	0.32	1.41176470588235	8
+4.8	3	1.4	0.1	"setosa"	14.4	0.14	1.6	14
+4.3	3	1.1	0.1	"setosa"	12.9	0.11	1.43333333333333	11
+5.8	4	1.2	0.2	"setosa"	23.2	0.24	1.45	6
+5.7	4.4	1.5	0.4	"setosa"	25.08	0.6	1.29545454545455	3.75
+5.4	3.9	1.3	0.4	"setosa"	21.06	0.52	1.38461538461538	3.25
+5.1	3.5	1.4	0.3	"setosa"	17.85	0.42	1.45714285714286	4.66666666666667
+5.7	3.8	1.7	0.3	"setosa"	21.66	0.51	1.5	5.66666666666667
+5.1	3.8	1.5	0.3	"setosa"	19.38	0.45	1.34210526315789	5
+5.4	3.4	1.7	0.2	"setosa"	18.36	0.34	1.58823529411765	8.5
+5.1	3.7	1.5	0.4	"setosa"	18.87	0.6	1.37837837837838	3.75
+4.6	3.6	1	0.2	"setosa"	16.56	0.2	1.27777777777778	5
+5.1	3.3	1.7	0.5	"setosa"	16.83	0.85	1.54545454545455	3.4
+4.8	3.4	1.9	0.2	"setosa"	16.32	0.38	1.41176470588235	9.5
+5	3	1.6	0.2	"setosa"	15	0.32	1.66666666666667	8
+5	3.4	1.6	0.4	"setosa"	17	0.64	1.47058823529412	4
+5.2	3.5	1.5	0.2	"setosa"	18.2	0.3	1.48571428571429	7.5
+5.2	3.4	1.4	0.2	"setosa"	17.68	0.28	1.52941176470588	7
+4.7	3.2	1.6	0.2	"setosa"	15.04	0.32	1.46875	8
+4.8	3.1	1.6	0.2	"setosa"	14.88	0.32	1.54838709677419	8
+5.4	3.4	1.5	0.4	"setosa"	18.36	0.6	1.58823529411765	3.75
+5.2	4.1	1.5	0.1	"setosa"	21.32	0.15	1.26829268292683	15
+5.5	4.2	1.4	0.2	"setosa"	23.1	0.28	1.30952380952381	7
+4.9	3.1	1.5	0.2	"setosa"	15.19	0.3	1.58064516129032	7.5
+5	3.2	1.2	0.2	"setosa"	16	0.24	1.5625	6
+5.5	3.5	1.3	0.2	"setosa"	19.25	0.26	1.57142857142857	6.5
+4.9	3.6	1.4	0.1	"setosa"	17.64	0.14	1.36111111111111	14
+4.4	3	1.3	0.2	"setosa"	13.2	0.26	1.46666666666667	6.5
+5.1	3.4	1.5	0.2	"setosa"	17.34	0.3	1.5	7.5
+5	3.5	1.3	0.3	"setosa"	17.5	0.39	1.42857142857143	4.33333333333333
+4.5	2.3	1.3	0.3	"setosa"	10.35	0.39	1.95652173913044	4.33333333333333
+4.4	3.2	1.3	0.2	"setosa"	14.08	0.26	1.375	6.5
+5	3.5	1.6	0.6	"setosa"	17.5	0.96	1.42857142857143	2.66666666666667
+5.1	3.8	1.9	0.4	"setosa"	19.38	0.76	1.34210526315789	4.75
+4.8	3	1.4	0.3	"setosa"	14.4	0.42	1.6	4.66666666666667
+5.1	3.8	1.6	0.2	"setosa"	19.38	0.32	1.34210526315789	8
+4.6	3.2	1.4	0.2	"setosa"	14.72	0.28	1.4375	7
+5.3	3.7	1.5	0.2	"setosa"	19.61	0.3	1.43243243243243	7.5
+5	3.3	1.4	0.2	"setosa"	16.5	0.28	1.51515151515152	7
+7	3.2	4.7	1.4	"versicolor"	22.4	6.58	2.1875	3.35714285714286
+6.4	3.2	4.5	1.5	"versicolor"	20.48	6.75	2	3
+6.9	3.1	4.9	1.5	"versicolor"	21.39	7.35	2.22580645161290	3.26666666666667
+5.5	2.3	4	1.3	"versicolor"	12.65	5.2	2.39130434782609	3.07692307692308
+6.5	2.8	4.6	1.5	"versicolor"	18.2	6.9	2.32142857142857	3.06666666666667
+5.7	2.8	4.5	1.3	"versicolor"	15.96	5.85	2.03571428571429	3.46153846153846
+6.3	3.3	4.7	1.6	"versicolor"	20.79	7.52	1.90909090909091	2.9375
+4.9	2.4	3.3	1	"versicolor"	11.76	3.3	2.04166666666667	3.3
+6.6	2.9	4.6	1.3	"versicolor"	19.14	5.98	2.27586206896552	3.53846153846154
+5.2	2.7	3.9	1.4	"versicolor"	14.04	5.46	1.92592592592593	2.78571428571429
+5	2	3.5	1	"versicolor"	10	3.5	2.5	3.5
+5.9	3	4.2	1.5	"versicolor"	17.7	6.3	1.96666666666667	2.8
+6	2.2	4	1	"versicolor"	13.2	4	2.72727272727273	4
+6.1	2.9	4.7	1.4	"versicolor"	17.69	6.58	2.10344827586207	3.35714285714286
+5.6	2.9	3.6	1.3	"versicolor"	16.24	4.68	1.93103448275862	2.76923076923077
+6.7	3.1	4.4	1.4	"versicolor"	20.77	6.16	2.16129032258065	3.14285714285714
+5.6	3	4.5	1.5	"versicolor"	16.8	6.75	1.86666666666667	3
+5.8	2.7	4.1	1	"versicolor"	15.66	4.1	2.14814814814815	4.1
+6.2	2.2	4.5	1.5	"versicolor"	13.64	6.75	2.81818181818182	3
+5.6	2.5	3.9	1.1	"versicolor"	14	4.29	2.24	3.54545454545454
+5.9	3.2	4.8	1.8	"versicolor"	18.88	8.64	1.84375	2.66666666666667
+6.1	2.8	4	1.3	"versicolor"	17.08	5.2	2.17857142857143	3.07692307692308
+6.3	2.5	4.9	1.5	"versicolor"	15.75	7.35	2.52	3.26666666666667
+6.1	2.8	4.7	1.2	"versicolor"	17.08	5.64	2.17857142857143	3.91666666666667
+6.4	2.9	4.3	1.3	"versicolor"	18.56	5.59	2.20689655172414	3.30769230769231
+6.6	3	4.4	1.4	"versicolor"	19.8	6.16	2.2	3.14285714285714
+6.8	2.8	4.8	1.4	"versicolor"	19.04	6.72	2.42857142857143	3.42857142857143
+6.7	3	5	1.7	"versicolor"	20.1	8.5	2.23333333333333	2.94117647058824
+6	2.9	4.5	1.5	"versicolor"	17.4	6.75	2.06896551724138	3
+5.7	2.6	3.5	1	"versicolor"	14.82	3.5	2.19230769230769	3.5
+5.5	2.4	3.8	1.1	"versicolor"	13.2	4.18	2.29166666666667	3.45454545454545
+5.5	2.4	3.7	1	"versicolor"	13.2	3.7	2.29166666666667	3.7
+5.8	2.7	3.9	1.2	"versicolor"	15.66	4.68	2.14814814814815	3.25
+6	2.7	5.1	1.6	"versicolor"	16.2	8.16	2.22222222222222	3.1875
+5.4	3	4.5	1.5	"versicolor"	16.2	6.75	1.8	3
+6	3.4	4.5	1.6	"versicolor"	20.4	7.2	1.76470588235294	2.8125
+6.7	3.1	4.7	1.5	"versicolor"	20.77	7.05	2.16129032258065	3.13333333333333
+6.3	2.3	4.4	1.3	"versicolor"	14.49	5.72	2.73913043478261	3.38461538461538
+5.6	3	4.1	1.3	"versicolor"	16.8	5.33	1.86666666666667	3.15384615384615
+5.5	2.5	4	1.3	"versicolor"	13.75	5.2	2.2	3.07692307692308
+5.5	2.6	4.4	1.2	"versicolor"	14.3	5.28	2.11538461538462	3.66666666666667
+6.1	3	4.6	1.4	"versicolor"	18.3	6.44	2.03333333333333	3.28571428571429
+5.8	2.6	4	1.2	"versicolor"	15.08	4.8	2.23076923076923	3.33333333333333
+5	2.3	3.3	1	"versicolor"	11.5	3.3	2.17391304347826	3.3
+5.6	2.7	4.2	1.3	"versicolor"	15.12	5.46	2.07407407407407	3.23076923076923
+5.7	3	4.2	1.2	"versicolor"	17.1	5.04	1.9	3.5
+5.7	2.9	4.2	1.3	"versicolor"	16.53	5.46	1.96551724137931	3.23076923076923
+6.2	2.9	4.3	1.3	"versicolor"	17.98	5.59	2.13793103448276	3.30769230769231
+5.1	2.5	3	1.1	"versicolor"	12.75	3.3	2.04	2.72727272727273
+5.7	2.8	4.1	1.3	"versicolor"	15.96	5.33	2.03571428571429	3.15384615384615
+6.3	3.3	6	2.5	"virginica"	20.79	15	1.90909090909091	2.4
+5.8	2.7	5.1	1.9	"virginica"	15.66	9.69	2.14814814814815	2.68421052631579
+7.1	3	5.9	2.1	"virginica"	21.3	12.39	2.36666666666667	2.80952380952381
+6.3	2.9	5.6	1.8	"virginica"	18.27	10.08	2.17241379310345	3.11111111111111
+6.5	3	5.8	2.2	"virginica"	19.5	12.76	2.16666666666667	2.63636363636364
+7.6	3	6.6	2.1	"virginica"	22.8	13.86	2.53333333333333	3.14285714285714
+4.9	2.5	4.5	1.7	"virginica"	12.25	7.65	1.96	2.64705882352941
+7.3	2.9	6.3	1.8	"virginica"	21.17	11.34	2.51724137931034	3.5
+6.7	2.5	5.8	1.8	"virginica"	16.75	10.44	2.68	3.22222222222222
+7.2	3.6	6.1	2.5	"virginica"	25.92	15.25	2	2.44
+6.5	3.2	5.1	2	"virginica"	20.8	10.2	2.03125	2.55
+6.4	2.7	5.3	1.9	"virginica"	17.28	10.07	2.37037037037037	2.78947368421053
+6.8	3	5.5	2.1	"virginica"	20.4	11.55	2.26666666666667	2.61904761904762
+5.7	2.5	5	2	"virginica"	14.25	10	2.28	2.5
+5.8	2.8	5.1	2.4	"virginica"	16.24	12.24	2.07142857142857	2.125
+6.4	3.2	5.3	2.3	"virginica"	20.48	12.19	2	2.30434782608696
+6.5	3	5.5	1.8	"virginica"	19.5	9.9	2.16666666666667	3.05555555555556
+7.7	3.8	6.7	2.2	"virginica"	29.26	14.74	2.02631578947368	3.04545454545455
+7.7	2.6	6.9	2.3	"virginica"	20.02	15.87	2.96153846153846	3
+6	2.2	5	1.5	"virginica"	13.2	7.5	2.72727272727273	3.33333333333333
+6.9	3.2	5.7	2.3	"virginica"	22.08	13.11	2.15625	2.47826086956522
+5.6	2.8	4.9	2	"virginica"	15.68	9.8	2	2.45
+7.7	2.8	6.7	2	"virginica"	21.56	13.4	2.75	3.35
+6.3	2.7	4.9	1.8	"virginica"	17.01	8.82	2.33333333333333	2.72222222222222
+6.7	3.3	5.7	2.1	"virginica"	22.11	11.97	2.03030303030303	2.71428571428571
+7.2	3.2	6	1.8	"virginica"	23.04	10.8	2.25	3.33333333333333
+6.2	2.8	4.8	1.8	"virginica"	17.36	8.64	2.21428571428571	2.66666666666667
+6.1	3	4.9	1.8	"virginica"	18.3	8.82	2.03333333333333	2.72222222222222
+6.4	2.8	5.6	2.1	"virginica"	17.92	11.76	2.28571428571429	2.66666666666667
+7.2	3	5.8	1.6	"virginica"	21.6	9.28	2.4	3.625
+7.4	2.8	6.1	1.9	"virginica"	20.72	11.59	2.64285714285714	3.21052631578947
+7.9	3.8	6.4	2	"virginica"	30.02	12.8	2.07894736842105	3.2
+6.4	2.8	5.6	2.2	"virginica"	17.92	12.32	2.28571428571429	2.54545454545454
+6.3	2.8	5.1	1.5	"virginica"	17.64	7.65	2.25	3.4
+6.1	2.6	5.6	1.4	"virginica"	15.86	7.84	2.34615384615385	4
+7.7	3	6.1	2.3	"virginica"	23.1	14.03	2.56666666666667	2.65217391304348
+6.3	3.4	5.6	2.4	"virginica"	21.42	13.44	1.85294117647059	2.33333333333333
+6.4	3.1	5.5	1.8	"virginica"	19.84	9.9	2.06451612903226	3.05555555555556
+6	3	4.8	1.8	"virginica"	18	8.64	2	2.66666666666667
+6.9	3.1	5.4	2.1	"virginica"	21.39	11.34	2.22580645161290	2.57142857142857
+6.7	3.1	5.6	2.4	"virginica"	20.77	13.44	2.16129032258065	2.33333333333333
+6.9	3.1	5.1	2.3	"virginica"	21.39	11.73	2.22580645161290	2.21739130434783
+5.8	2.7	5.1	1.9	"virginica"	15.66	9.69	2.14814814814815	2.68421052631579
+6.8	3.2	5.9	2.3	"virginica"	21.76	13.57	2.125	2.56521739130435
+6.7	3.3	5.7	2.5	"virginica"	22.11	14.25	2.03030303030303	2.28
+6.7	3	5.2	2.3	"virginica"	20.1	11.96	2.23333333333333	2.26086956521739
+6.3	2.5	5	1.9	"virginica"	15.75	9.5	2.52	2.63157894736842
+6.5	3	5.2	2	"virginica"	19.5	10.4	2.16666666666667	2.6
+6.2	3.4	5.4	2.3	"virginica"	21.08	12.42	1.82352941176471	2.34782608695652
+5.9	3	5.1	1.8	"virginica"	17.7	9.18	1.96666666666667	2.83333333333333

serie1/rapport.md

+# Perceptron learning rule
+simon.cirilli@hes-so.ch - kiady.arintsoa@hes-so.ch
+## lineparams
+This function is usefull to translate the weights and bias into a line equation. It returns the slope and the intercept of the line.
+## predict 
+This function is used to predict the class of a point. It returns 1 if the point is above the line and 0 if the point is below the line.
+## Update weights and bias
+This function is used to update the weights and bias. It returns the new weights and bias and this is from the perceptron learning rule.
+## Train code
+The train code is used to train the perceptron. The stop criterion is the number of iterations. The function returns the weights and bias and the number of misclassifications per iteration.
+## Test
+We see that we have a pretty good line separating the two classes. 
+
+## plot with the misclassifications per iteration
+![](learning.png)
+
+# To summarize
+So we have seen that the perceptron learning rule is able to find a separating line for linearly separable data. We also saw that in 28 iterations the algorithm was able to find almost the correct separating line. We can see from the misclassifications plot that the number of misclassifications decreases with each iteration. But sometimes the number of misclassifications increases. This is because when we update the weights and bias, we are not always updating in the perfect right direction.
\ No newline at end of file